The US stock market showed a strong rebound, overcoming negative factors such as concerns over the prolonged Iran conflict, rising oil prices, and increasing Treasury yields. Major indices, including the S&P 500, Nasdaq, and the Russell 2000 (composed of small and mid-cap stocks), all hit new all-time highs. In particular, the New York Stock Exchange opened with bullish sentiment before the bell on the 30th, as key economic indicators showed better-than-expected performance. The first-quarter GDP growth rate was recorded at 2.0% on an annualized basis, lower than the experts' forecast of 2.2%, but it showed a significant recovery compared to 0.5% in the fourth quarter of the previous year. While consumer spending showed some slowdown, corporate investment surged to 10.4%, the highest level in three years, driving economic growth. Among these, the expansion in equipment investment and intellectual property investment was directly caused by the expansion in AI-related investment, while government spending also recovered, offsetting the decline due to the shutdown. Private spending on private purchasing power also recorded a higher growth rate than the previous quarter, proving the solid foundation of the US economy.
The PCE, a measure of inflation, rose 3.5% year-on-year, reflecting rising energy prices, and hit a new high not seen in three years. However, core inflation, excluding energy and food, was at 3.2%, with little difference from expectations. Real consumption is also following a favorable trend as income growth and consumption recovery offset the rise in prices. Claims for unemployment benefits fell to the lowest level since 1969, and the employment cost index exceeded expectations, showing that the labor market remains robust. Experts believe that through these data, the US economy has completely moved past the sluggish end of last year and entered a rebound phase. They also forecast that the expansion of corporate investment in AI will continue to act as a growth engine in the future. In particular, since corporations are pouring massive capital into building AI infrastructure, analysis is dominated by the view that this is providing a strong tailwind to industries related to semiconductors, power, and data centers.
The earnings reports of technology-led hyperscalers clarified the criteria for investors' choices. Google recorded a 63% growth rate, far exceeding expectations for cloud revenue, and completed a profitable business model by directly selling its own AI chip, the TPU. Accordingly, JP Morgan maintained a buy rating for Google, evaluating it as a top pick. Conversely, Meta faced a headwind with its stock price falling 9% despite a 33% surge in revenue. This is because Wall Street judged that it would be difficult to generate immediate revenue from massive AI capital expenditure beyond advertising revenue. Microsoft also saw high growth in Azure cloud, but its growth rate lagged behind competitor Google, and its stock price fell because the subscriber growth rate for Copilot failed to meet expectations. Amazon led its stock price rise with accelerating AWS revenue, but it is leveraging cost-cutting effects from developing its own chips to secure long-term superiority.
Such market trends have had complex effects on semiconductor companies like NVIDIA. Concerns grew that the dependence on NVIDIA could decrease as big tech companies such as Google, Amazon, and Microsoft focus on developing their own chips. Furthermore, it was raised that these companies might focus on solving the burden of soaring memory prices rather than spending massive capital on GPU purchases. Ultimately, while NVIDIA's stock price switched to a downward trend, stock prices for memory companies and energy facilities needed for data center construction surged. Qualcomm's stock price surged by 15% despite lowering its earnings guidance, as its own chip project proceeded smoothly. The market came to realize that the AI boom is not in its early stages but that infrastructure construction is at a dead end, which foreshadows fierce competition between technological innovation and capital efficiency in the future.