2026年9月14日 / 美国东部时间下午2:58 / 哥伦比亚广播公司新闻
作者
梅根·塞鲁洛 记者，MoneyWatch栏目
梅根·塞鲁洛是总部位于纽约的CBS MoneyWatch记者，报道小企业、职场、医疗保健、消费者支出和个人理财话题。她经常做客哥伦比亚广播公司新闻24小时频道讨论其报道内容。
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一些华尔街分析师认为，推动美国股市创下历史新高的人工智能泡沫正在开始收缩。
“有大量迹象表明，我们目前正处于人工智能泡沫的后期阶段，”资本经济公司金融市场首席经济顾问约翰·希金斯在周一的一份报告中表示。
这家投资咨询公司的“最佳猜测”是，人工智能泡沫将于2027年开始破裂。资本经济公司还预测，明年标普500股票指数将出现回调，即股价较最近高点至少下跌20%。
“如果对比领先人工智能公司的预期盈利增速和美国经济增速，从诸多指标来看，它们的估值显得非常紧绷——这又是一次互联网泡沫，”资本经济公司高级市场经济学家詹姆斯·赖利告诉哥伦比亚广播公司新闻。“虽然我们相信人工智能将带来变革并产生利润，但我们认为利润不会像分析师预期的那样高。”
根据高盛集团的数据，2026年全球人工智能相关项目的资本支出预计将达到1万亿美元，其中美国占5810亿美元。这场热潮推动股市在过去两年经历了一轮史诗级上涨，投资者纷纷跟风押注，期待领先人工智能企业未来能实现丰厚利润。
泡沫的棘手之处
经济学家警告称，识别投资泡沫——也就是投机过度的时期——本就困难，更遑论预测泡沫何时破裂。
“我们必须非常谨慎地使用泡沫这个术语，具体来说，因为每一次技术革命的初期往往都会伴随大量投资，”安永帕特农首席经济学家格雷格·达科告诉哥伦比亚广播公司新闻。“但与此同时，市场往往会出现过度投机。由于一项新技术极具吸引力，并承诺彻底改变我们的做事方式，市场常常会出现狂热情绪。”
达特茅斯学院塔克商学院的投资策略师肯尼思·R·弗伦奇表示，投资者通常会在某项技术的全部经济影响显现之前很久就大举涌入热门科技股。他不太相信人工智能热潮会在短期内失去动力。
“人们变得乐观起来，五年后我们回头看时，可能会说当时人们对人工智能过于悲观，而这项技术的重要性超出了我们的预期，”他告诉哥伦比亚广播公司新闻。“它已经对盈利产生了巨大影响，所以我们很可能低估了人工智能的积极影响。”
“我们没有足够的信息来判断当前的股价是合理还是不合理，是过高还是过低，”弗伦奇补充道。
更确定的是，在人工智能研究人员和企业领袖纷纷警告该技术构成的威胁之际，公众对人工智能的看法正在转变。这些担忧，加上行业领军人物呼吁放缓人工智能开发的呼声，可能会给科技股蒙上阴影。
但达科指出，对人工智能缺乏监管保障的担忧，与对该技术推动企业利润潜力的不确定性是两码事。“这与对泡沫的担忧略有不同。这更多是对没有建立合适的监管保障来管控技术、避免技术本身出现过度行为的担忧——而非针对投资和回报。”
本文编辑：阿兰·谢特尔
The AI bubble is leaking air, some economists say. Should investors worry?
September 14, 2026 / 2:58 PM EDT / CBS News
By
Megan Cerullo Reporter, MoneyWatch
Megan Cerullo is a New York-based reporter for CBS MoneyWatch covering small business, workplace, health care, consumer spending and personal finance topics. She regularly appears on CBS News 24/7 to discuss her reporting.
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The AI bubble that has propelled U.S. stocks to record highs is starting to deflate, according to some Wall Street analysts.
“There are plenty of signs that we are now in the late stages of a bubble in AI,” John Higgins, chief economic adviser for financial markets at Capital Economics, said in a report on Monday.
The investment advisory firm’s “best guess” is that the AI bubble will begin to burst in 2027. Capital Economics also forecasts a correction, or when stocks fall at least 20% from their most recent high, in the S&P 500 stock index next year.
“If you look at the rate at which leading AI firms’ earnings are expected to grow, versus how fast the U.S. economy has been growing, by many measures they look really stretched — as in this is the dot-com bubble all over again,” Capital Economics senior markets economist James Reilly told CBS News. “And while we believe AI will be transformative and produce profits, we don’t think they’ll be as high as analysts are expecting.”
In 2026, global capital expenditures on AI-related projects are projected to hit $1 trillion, including $581 billion in the U.S., according to Goldman Sachs. That boom has fueled an epic run for stocks over the last two years, with investors hopping on the bandwagon in anticipation of robust future profits from companies leading the way in AI.
The trouble with bubbles
Economists caution that it is difficult to identify investment bubbles, or periods of speculative excess, let alone predict when one will pop.
“We have to be very careful with the bubble terminology, specifically, because every type of technological revolution tends to have a great dose of investment in the first phase,” EY-Parthenon chief economist Greg Daco told CBS News. “But at the same time, there are often excesses. There is often exuberance because a new technology is very attractive and promises to revolutionize the way we do things.”
Kenneth R. French, an investment strategist at Dartmouth College’s Tuck School of Business, said investors commonly pile into hot tech stocks long before a technology’s full economic impact becomes clear. He is less convinced that the AI boom is at risk of losing steam anytime soon.
“People get optimistic, and it’s conceivable that five years from now, we’ll be looking back and saying people were pessimistic about AI, and that it was more important than we expected,” he told CBS News. “It’s already having a huge impact on earnings, so it could really be that we have underestimated AI’s positive impact.”
“We don’t have enough information to judge if these prices are right or wrong, too high or too low,” French added.
More certain is that the public narrative around AI is shifting amid a slew of warnings from AI researchers and corporate leaders about the threat posed by the technology. Those concerns, along with calls from leading industry figures for a slowdown in AI development, could cast a pall over tech stocks.
Yet concerns about the lack of guardrails around AI are distinct from uncertainty over the technology’s potential to drive corporate profits, Daco noted. “That’s slightly different than a bubble fear. It’s more fear of not having the right guardrails to control tech and avoid excesses of the tech itself — not about investments and returns.”
Edited by Alain Sherter
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