Goldman Sachs Asset Management: AI financing concerns are a "false alarm" with trillions in capital expenditure backed by cash flow from tech giants.

Goldman Sachs Group, Inc. asset management public technology investment and joint head of US basic equity, Sung Cho, recently discussed with Scott Wapner and delivered a core and reassuring message: the artificial intelligence financing anxiety sparked by the perceived fragility of enterprise valuations and fluctuations in the current market is largely exaggerated. This conversation delved deeper into the trajectory of AI investment trading, broader market prospects, and the underlying stability of the industry's capital structure, providing a nuanced perspective for founders, venture capitalists, and industry insiders exploring this era of transformation. Cho's core insight challenges the narrative of an impending AI bubble by analyzing the sources driving the current prosperity. He believes that the majority of AI investments do not rely on speculative debt, but on strong internal cash flows generated by mature tech giants. This distinction is crucial for understanding the long-term resilience of the AI market. Regarding the significant capital flowing into AI, Cho stated, "Considering the total levels of spending we need in the coming years, we believe it's around $700 billion to $1 trillion. What should alleviate fears is that 90% of this is supported by operating cash flows." The significant reliance on high-profit internal capital (rather than external debt) highlights the fundamental advantage of AI investment, making it different from previous tech speculation cycles. The impact is profound: companies are funding their AI ambitions from a financially healthy perspective, rather than leveraging themselves into precarious situations. Furthermore, even the small portion of AI investments through debt financing is primarily stable. Cho emphasized that a considerable proportion of this debt is issued by high-rated entities. He explained, "In reality, only 10% is debt-funded. And you know, most of this debt is actually issued by Meta, which has a credit rating higher than the US government, and their bond trading spreads are even lower than US treasuries." This indicates that debt financing in the AI field is not concentrated on speculative enterprises with unstable balance sheets, but on mature companies with high credit ratings, further reducing systemic risks. Concerns about specific companies such as Coreweave and Oracle Corporation that have recently experienced market fluctuations were also addressed objectively. Cho clarified that these situations do not indicate a weakening overall demand for AI. Instead, they often reflect operational challenges or supply chain bottlenecks. He explained, "I don't think funding will be a problem by 2026. If you think about Coreweave and Oracle Corporation, I think you also have to recognize, it's not a demand issue they're facing. They're trying to deal with some supply chain backlog." This distinction is crucial for investors, indicating that setbacks of specific companies do not foreshadow broader industry decline but are temporary and solvable operational obstacles. Cho's second key insight involves the dynamic nature and inherent volatility of the leadership position in the foundational AI model field. The rapid turnover of perceived leaders in the AI model field is a defining feature of this nascent market. Today's apparent dominator could be surpassed tomorrow. Cho pointed out the rapid shifts in investor sentiment and market valuations among leading AI participants. He observed, "Think about the emotional shifts surrounding different frontrunners in the model field during 2025. If you look at the beginning of 2025, Meta was seen as the dominant model...six months later, OpenAI was viewed as the frontrunner...and recently, in the eyes of investors, Alphabet Inc. Class C is taking the lead." This fluidity of competition, while producing notable volatility in individual stocks, also signifies intense innovation and competition for technological dominance, rather than a collapse of the market as a whole. The financial impact of this "model volatility" is significant. For example, the recent surge in market capitalization of Alphabet Inc. Class C is a direct result of this shift in perception. Cho stated, "It's because people believe that Gemini is now taking the lead, their market cap has increased by a trillion dollars in the past three months." This significant short-term market cap growth highlights how investor confidence and capital can swiftly shift based on perceived technological advantages. This dynamic environment will continue, with new advances reshaping the competitive landscape. The emergence of next-generation AI models, such as those trained on the NVIDIA Corporation Blackwell architecture in early 2026, is expected to bring further volatility and potential shifts in leadership. Therefore, as innovation cycles accelerate and new capabilities emerge, market participants are likely to perceive continued shifting of dominant positions. The market is not stagnant but evolving at an unprecedented pace. Ultimately, Cho's analysis paints a picture of the AI market: one in which the underlying capital is ample, supported primarily by sustainable operating cash flows rather than precarious leverage. While competition dynamics among leading AI model developers are expected to remain volatile, these shifts are a natural outcome of intense innovation, rather than an indicator of systemic financial weakness. While these anxieties are apparent, they appear to be largely exaggerated from the perspective of fundamental financial health and demand.