US tech stocks hit by AI boom concerns after OpenAI, MIT warnings
US tech stocks experienced a notable downturn this week, as a growing chorus of concerns over the longevity and sustainability of the artificial intelligence boom began to puncture Wall Street’s once-unbridled enthusiasm. The shift in sentiment was significantly influenced by a stark warning from OpenAI CEO Sam Altman and the sobering findings of a recent Massachusetts Institute of Technology (MIT) paper, prompting investors to reassess the frothy valuations that have propelled the market for months.
OpenAI CEO Sam Altman, a central figure in the AI revolution, explicitly cautioned that the AI sector might be entering “bubble” territory, suggesting that investors have become “overexcited.” Drawing parallels to the dot-com bust of the late 1990s, Altman articulated that while the underlying AI technology is genuinely transformative, the current market valuations appear increasingly detached from reality, leading to a potential scenario where “someone’s gonna get burned.” He elaborated that during such speculative periods, even “smart people get overexcited and misallocate capital,” funding startups that may lack viable long-term prospects. This caution from an industry insider, whose company recently launched GPT-5, carries significant weight, even as OpenAI itself plans to invest trillions of dollars into data center infrastructure, acknowledging the immense capital expenditure required for advanced AI.
Adding to the market’s unease are the revelations from an MIT Technology Review investigation, published in May 2025, which highlighted the explosive energy demands and environmental footprint of the burgeoning AI industry. The report underscores that AI’s voracious appetite for electricity is rapidly escalating, with data centers projected to consume between 12% and 15% of the total U.S. electricity by 2030, a significant jump from 4% in 2023. This surge is already exerting pressure on existing power grids and contributing to rising electricity prices for both consumers and businesses. Even seemingly minor interactions, such as the “politeness” embedded in AI prompts, translate into millions of dollars in operational expenses, according to Altman himself. These findings introduce a critical sustainability question, suggesting a “hidden environmental tax” that could challenge the long-term economic viability of some AI initiatives.
The confluence of these warnings has visibly impacted market dynamics. After a period where “Magnificent 7” tech giants, heavily invested in AI, largely drove the S&P 500’s gains, analysts are now observing signs of moderation. The fear is palpable: if AI’s real-world delivery in the near term fails to meet the inflated expectations, a sharp market correction could ensue. Valuation metrics, such as the S&P 500’s price-to-book ratio, which has surpassed dot-com era highs, and forward price-to-earnings multiples nearing 1929 levels, further fuel concerns of overvaluation. While the massive AI spending spree by Big Tech, projected to exceed $350 billion this year on data centers, has been a significant economic driver, it also renders the U.S. economy increasingly reliant on the sustained growth of the AI sector. This growing dependency raises a critical vulnerability should the AI boom decelerate, signaling a potential “bull market breather” as major indices have recently seen flat or slightly negative performance.
The current market environment reflects a pivotal moment, transitioning from pure AI euphoria to a more discerning, cautious outlook. While AI’s transformative potential remains undisputed, the industry is now confronting the complex realities of unsustainable valuations, immense infrastructure costs, and significant environmental impacts. The coming months will test whether the AI sector can translate its groundbreaking technological advancements into tangible, sustainable economic returns, or if a more substantial correction awaits.