While Wall Street continues to embrace AI, a recent shift indicates that the "unconditional-love phase" of this romance is over. Investors are now seeking better answers and tangible returns before they continue to underwrite hundreds of billions of dollars in investments for data centers and AI models, a sentiment echoed by the experiences of Meta and Google. This growing scrutiny suggests a move towards a more discerning approach to AI investments.
The AI trade has experienced a significant downturn, yet the broader bull market has largely remained resilient. This unusual scenario is partly explained by the tech sector's forward price-to-earnings (P/E) ratio, which fell approximately 30% from a year prior at its July low, a drop comparable to those seen during the dot-com bust and the financial crisis. Despite this, the S&P 500 remained near a record high. This apparent contradiction arose because tech prices rose roughly 40% while expected earnings jumped approximately 80% over the past year, meaning earnings growth outpaced price increases, making stocks cheaper even as they climbed.
However, this positive trend hinges entirely on these projected profits materializing. Big Tech companies are pouring immense capital into chips, data centers, networking, and power infrastructure. Investors are increasingly questioning who will truly benefit from this AI spending boom and who will bear the costs. If AI capacity becomes overbuilt, customer spending slows, chip pricing weakens, or economic downturns impact corporate tech budgets, future profit forecasts could be slashed.
Should these profit forecasts be cut, the situation could reverse sharply. For instance, a stock trading at $140 with expected earnings of $9 (a P/E of 16 times) would suddenly cost more than 23 times earnings if the forecast dropped to $6, even without any change in its stock price. This highlights the vulnerability of current valuations to future earnings delivery, making the arrival of expected profits the crucial factor for the bull case.
