Goldman Sachs: AI Trading Bubble Theory Is Premature, Current Market Driven by Earnings

BlockBeats News, July 2nd. Goldman Sachs believes that investors should not exit the artificial intelligence theme prematurely. Despite the significant gains of AI-related stocks so far this year and the increasing market concerns about a bubble, Ben Snider, Goldman Sachs' U.S. stock strategy director, stated that the current market trend still resembles a profit-driven bull market rather than a speculative frenzy solely supported by valuation expansion.

Snider believes that AI investment opportunities still focus on three main themes: AI infrastructure, power infrastructure, and cloud providers and hyperscale platforms that have recently underperformed. The first theme includes semiconductors, servers, AI networking equipment, and data center hardware; the second theme includes power equipment, utilities, and energy infrastructure that support data center expansion; the third theme includes large platform companies such as Amazon, Microsoft, Meta, Alphabet, Oracle, and IBM.

Goldman Sachs' core argument is that although many AI infrastructure stocks have seen large gains, their valuations have not detached from earnings expectations like in a typical bubble. Some storage, semiconductor, and data center-related stocks still reflect market skepticism rather than unanimous optimism. In other words, investors are still demanding these companies to prove with orders, profit margins, and cash flow that AI capital expenditures can translate into profitability.

Goldman Sachs is not recommending indiscriminately buying all AI concept stocks but rather continuing to bet on companies that can directly benefit from AI capital spending in terms of revenue and profit growth. It believes that AI trading has not yet entered the stage of "storytelling-only, disregarding earnings." As long as earnings continue to rise, AI infrastructure may still remain one of the most important themes in the U.S. stock market.