2026年6月30日 美国东部时间上午9:48 / CBS财经观察栏目
作者：梅根·塞鲁洛 记者，MoneyWatch
梅根·塞鲁洛是驻纽约的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CBS新闻24/7频道讨论其报道内容。
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2026年6月30日 / 美国东部时间上午9:48 / MoneyWatch
人工智能有朝一日或许能强化人类认知，推动科学、技术及其他领域取得重大进步。但一项最新学术研究显示，它也可能让我们变得乏味。
这是因为驱动人工智能应用的所谓大型语言模型，往往会输出符合整体人群预期和常规认知的信息，将生活的复杂性简化为一堆平淡无味、经过稀释的观点。
“大型语言模型会预测句子中最有可能出现的下一个词，或是序列中最有可能发生的下一个事件，而从定义上来说，这就是平均水平的结果，”该研究的作者、哥伦比亚商学院教授桑德拉·马茨在接受CBS新闻采访时表示。“当你让它推荐电影或是墙面该刷什么颜色时，它只会告诉你最有可能出现的答案。它会让决策同质化，我们得到的都会是同样的输出结果。”
为了得出研究结论，马茨和她的合著者分析了1000名受试者做出的超过11万个真实决策，并将其与通用型和个性化人工智能代理做出的选择进行了对比。他们还使用了名为“我的人格”的Facebook应用项目的数据，该项目曾对分享个人Facebook档案用于研究的用户进行人格测试。
“人工智能厌恶风险”
马茨是一位兼具心理学和计算机科学背景的计算社会科学家，她表示，对于个人而言，依靠人工智能做决策——比如选择度假地点或是购买哪款步行鞋——会引导人们倾向于最常见的选择，远离更具个性、甚至有些古怪的行为和偏好。
实际上，人工智能会限制用户“在不同话题和心理偏好上的探索范围”，她补充道，“大型语言模型会在用户的偏好范围内追求稳妥。”
换句话说，即便人工智能代理知道用户偶尔会在特定话题上做出反常规或是不符合一贯风格的决定，比如晚餐吃什么，“大型语言模型还是会引导行为偏向更常规的选项，缩小个人的探索范围”，马茨补充道。
“人工智能厌恶风险，因为我们就是这样训练它的，”马茨说。“它希望让你留在平台上，所以只会向你展示你已经喜欢的东西，而不会涉及你兴趣范围之外的内容。”
她补充道，人工智能应用并非必须如此运作，但这是目前它们的编程逻辑。
为了防止人工智能代理削弱人类体验的丰富性和多样性，马茨鼓励科技开发者为希望获得更多意外、非常规推荐的用户增加“探索模式”选项。
这将有助于确保“我们防止个人自己变得乏味，同时确保文化不会坍缩为单一的偏好集合”，马茨说道。
编辑：阿兰·谢特
AI could make people dull, one scientist fears. Here’s why.
2026-06-30 9:48 AM EDT / CBS MoneyWatch
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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June 30, 2026 / 9:48 AM EDT / MoneyWatch
Artificial intelligence could one day supercharge human cognition, leading to significant advances in science, technology and other fields. It could also make us dull, new academic research suggests.
That’s because the so-called large language models that power AI apps often yield information that is predictable and normative for the population as a whole, reducing life’s complexity to a bland mulch of watered-down ideas.
“LLMs predict the most likely next word in a sentence or event in a sequence, and by definition, that’s average,” Columbia Business School professor Sandra Matz, author of the study, told CBS News. “It tells you what the most likely thing to appear is if you ask it for a movie recommendation or what color to paint your wall. It homogenizes decisions, and we all get the same output.”
To arrive at their findings, Matz and her study co-authors analyzed more than 110,000 real-world decisions made by 1,000 people and compared them to choices made by both generic and personalized AI agents. They also used data from a Facebook application called the myPersonality project, which conducted personality tests on users who shared their Facebook profiles for research purposes.
“AI hates risk”
For individuals, relying on AI to shape a decision — say, on where to go on vacation or what walking shoes to buy — steers people toward the most common choices and away from more distinctive, or even quirky, behaviors and preferences, according to Matz, a computational social scientist with a background in psychology and computer science.
In effect, AI narrows what users “explore across topics and psychological affinities,” she wrote, adding that “LLMs play it safe within a user’s preferences.”
In other words, even if an AI agent knows its user occasionally makes an out-of-the-box or uncharacteristic decision about any given subject, like what to eat for dinner, “LLM agents nudge behavior toward more normative options and narrow the range of what individuals explore,” Matz added.
“AI hates risk because we train it that way,” Matz said. “It wants to keep you on the platform, so it shows you what you already like and not stuff on the outskirts of what you do.”
AI apps don’t have to operate this way, but it’s how they’re programmed to work, she added.
To prevent AI agents from diminishing the richness and diversity of human experience, Matz encourages tech developers to build in an “exploration mode” option for users who want more unexpected, less conventional recommendations.
That would help ensure “we prevent ourselves as individuals from becoming boring, and making sure culture doesn’t collapse into a single set of preferences,” Matz said.
Edited by Alain Sherter