O
racle’s shares jumped 36 % in one day after the company announced a new partnership with OpenAI, adding roughly $244 billion to its market value. The rally has reignited fears of an AI‑driven asset bubble, echoing the dot‑com frenzy that saw the NASDAQ fall from 5,048 in March 2000 to 1,139 in October 2002.

    Michael Acton, head of research at AEW Capital Management, cautions that while rising valuations of AI firms do not automatically signal a bubble, they warrant scrutiny. He points to Warren Buffett’s metric of market‑cap‑to‑GDP, now at an all‑time high, far exceeding the levels seen during the early 2000s tech crash.

    The rapid spread of AI technologies coincides with a demographic plateau: population and labor‑force growth are slowing in most advanced economies. Economic expansion will increasingly hinge on productivity gains from labor substitution and augmentation. If the AI boom falters, commercial real‑estate demand could shift dramatically. Data‑center space, already a hot investment segment, would feel the most immediate pressure. Conversely, office tenants might still seek new hires if productivity gains from AI do not materialize, potentially offsetting some losses.

    A collapse of AI stocks could erode household net worth by converting trillions of equity gains into losses, disrupting consumption and investment for years. For real‑estate investors, falling equity values could trigger a “negative denominator” effect, forcing reductions in property holdings. Ryan Severino, chief economist at BGO, notes that such a scenario would also raise borrowing costs, as the U.S. would need to self‑fund its $2 trillion‑per‑year deficit, pushing interest rates higher and worsening the debt burden.

    Predicting a bubble is notoriously difficult; most recognition comes post‑facto. Yet bubbles can signal structural economic shifts. Christopher Thornberg of Beacon Economics argues that AI, like the Internet, will reshape the economy profoundly. Even if a bubble forms, the long‑term prospects of AI remain transformative, and investors who focus on that trajectory should ultimately benefit.

    Timothy Savage of NYU stresses that the AI boom’s potential collapse could trigger a broader public‑debt crisis. The surge in foreign portfolio investment chasing AI returns has kept interest rates low despite massive federal borrowing. A bubble burst would reverse this dynamic, forcing the U.S. to raise rates and increasing the cost of servicing debt.

    Chad Littell of CoStar highlights that the true bubble may lie not in AI itself but in the underlying hardware. Large language models (LLMs) require massive, energy‑intensive data centers. If future algorithms become more parallelizable, the current footprint of data centers could become overbuilt, reducing their value.

    John Chang of Marcus & Millichap compares the current data‑center landscape to the dot‑com era. Between 1990 and 2000, fewer than 40 data centers exceeded 200,000 sq ft nationwide, and only eight surpassed 500,000 sq ft. Today, 282 data centers are either built or under construction, with 79 exceeding 500,000 sq ft and some reaching four million sq ft. The largest projects are spread across Washington, DC, Atlanta, Richmond, Omaha, Des Moines, Chicago, Phoenix, Dallas, and Albuquerque. A bubble burst would therefore affect a wide geographic spread rather than a single market.

    Despite lofty valuations, the risk that an AI bubble will devastate commercial real estate is limited compared to the dot‑com crash. AI is already embedded across mature industries, reducing its vulnerability. Moreover, 50–67 % of tech workers now work remotely, a stark contrast to the single‑digit rates in 2000, lessening the impact on office demand. AI talent is also more geographically dispersed than the early‑2000s dot‑com workforce, which was heavily concentrated in the Bay Area. Consequently, any bubble burst would spread its effects across many markets, diluting the direct impact on any single sector.

    In sum, while an AI bubble could trigger significant financial market turbulence and potentially a recession, its direct damage to commercial real‑estate markets would likely be muted. Investors should prepare for a structurally altered future rather than fixate on the possibility of a bubble, as the long‑term benefits of AI remain substantial.