Goldman Sachs warns: AI profit surprises are about to ebb, US stock earnings season may enter a new phase of "expectation validation"

Christian Mueller-Glissmann, head of asset allocation research at Goldman Sachs, stated that the previous earnings season saw a surge in profits driven by artificial intelligence (AI), which led to a significant increase in US stocks. However, he believes that this trend may not be repeated this season, as relying solely on corporate performance is no longer enough to trigger a significant market rebound. Although it is likely that publicly listed companies will continue to exceed expectations, Mueller-Glissmann mentioned that the benchmark for this earnings season has been significantly raised. Investors will pay more attention to future performance guidance and management commentary in order to capture signals of whether stock indices can continue to slowly climb on the existing foundation. According to Goldman Sachs' forecast, S&P 500 companies' earnings growth in the second quarter will reach 22%, a figure that aligns closely with market expectations. However, the key issue is not if companies can exceed expectations, but by how much and whether the market will respond positively to the outperformance. The surprise in the previous quarter mainly came from the "non-linear surge" in the AI-related supply chain, especially in the explosive demand for chips, servers, and cloud infrastructure. Current market expectations have already priced in these positives, and the valuations of some leading stocks imply high growth assumptions. Even if there are positive surprises this season, the marginal impact is expected to weaken significantly and the market's response to "better than expected" results may become subdued. Mueller-Glissmann further explained that usually in the late stage of a cycle, upward momentum in earnings revisions can last for a long time, but the massive wave of profit surprises linked to AI capital expenditures is likely coming to an end. The top US tech companies have planned capital expenditures totaling up to $725 billion this year in data centers, specialized chips, and network devices. Mueller-Glissmann believes that hyperscalers, as holders of large AI infrastructure assets, are still in a favorable competitive position, but they should focus more on improving operational efficiency and accelerating the commercialization of AI in the future. The focus for the second half of the year should shift to: increasing asset utilization - avoiding duplicate construction and idle processing power; strengthening pricing capabilities - converting processing power into measurable cloud service income; optimizing capital allocation - seeking a balance between investment and shareholder returns. "The overall structural trend of AI remains intact," Mueller-Glissmann emphasized, "but the market will no longer generously reward any AI-related stories, but will instead scrutinize each business for its return on investment."