Recently, US tech stocks have experienced significant volatility due to market concerns about the future of artificial intelligence among US tech giants. They felt anxious and questioned about the large-scale capital expenditures involved in the project.
However, this anxiety has been largely exaggerated—this is Goldman Sachs's perspective . The core message conveyed by Sung Cho, co-head of public technology investment and U.S. fundamental equities at asset management firm Public Technology Investment, in a recent interview.
The fear of an AI bubble is excessive.
Sung Cho challenged the claim that an " artificial intelligence bubble is about to burst." He argued that the vast majority of current AI investment is not based on speculative debt, but rather on the strong, internally generated cash flow of established tech giants . This distinction is crucial for understanding the long-term resilience of the AI market.
"If we consider the total investment we will need (in the field of artificial intelligence) over the next few years, it will be between $700 billion and $1 trillion. What is reassuring is that 90% of that funding comes from the company's operating cash flow."
This significant reliance on internal funding (rather than external debt) from highly profitable companies highlights a fundamental advantage of AI investment, distinguishing it from previous technology speculative cycles. The implications are far-reaching: companies fund their AI ambitions based on their financial health, rather than placing themselves in a precarious position.
Most debt issuers are mature companies with high credit ratings.
Furthermore, even the smaller portion of AI investments financed through debt is relatively safe and stable. Sung Cho emphasized that a significant portion of the AI industry's debt is issued by highly rated entities. He further explained:
"In reality, only 10% of the funds are financed through debt. And did you know that most of that debt is actually issued by Meta, which has a higher debt rating than the U.S. government and whose bonds are traded at prices even lower than U.S. Treasury bonds ?"
This indicates that debt financing in the field of artificial intelligence is not concentrated on speculative companies with poor balance sheets, but rather on mature companies with extremely high credit ratings, thereby further reducing systemic risk.
Significant fluctuations in individual stocks reflect specific operational challenges.
Sung Cho also discussed recent developments with Coreweave and Oracle. The significant stock price fluctuations of companies like Oracle, and the anxiety surrounding them, are concerning. Sung Cho clarified that the situation at companies like Oracle does not indicate a weakening of overall demand for artificial intelligence. Rather, it often reflects operational challenges or supply chain bottlenecks.
“I don’t think funding will be an issue in 2026. If you think about the problems with Coreweave and Oracle carefully, I think you have to realize that they are not facing a demand problem, but rather a bit of a supply chain backlog,” he explained. This distinction is crucial for investors, showing that a particular company’s setbacks are not a sign of an industry-wide downturn, but rather temporary, solvable operational obstacles.
Sung Cho's second key insight concerns the dynamics and inherent volatility of leadership in foundational AI models. In the field of AI models, the names of leaders are constantly changing rapidly: what seems dominant today may be overtaken tomorrow.
Sung Cho also acknowledged that the pace of change in the AI field is indeed very fast:
“Look at the shifts in sentiment towards different frontier models during 2025. If we start from the beginning of 2025, Meta is considered the dominant model… Six months later, OpenAI is seen as the dominant frontier model… And most recently, Google has taken the lead in the eyes of investors.”
He pointed out that while this dynamic competition brings remarkable volatility to individual stocks, it also signifies fierce innovation and a struggle for technological dominance, rather than a market-wide collapse.
The financial impact of this "model volatility" is enormous. For example, Google's recent surge in market capitalization is a direct result of this shift in sentiment.
Sung Cho argues that while the competitive landscape among leading AI model developers will remain highly volatile, these changes are a natural consequence of intense innovation, rather than a reflection of systemic financial weakness. From the perspective of underlying financial health and needs, these anxieties, while evident, appear exaggerated.
(Article source: CLS)
