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A new study by Columbia Business School professor Sandra Matz suggests that while artificial intelligence (AI) can enhance human cognition in various fields, it could also make people's decisions more homogeneous and risk-averse. Analyzing over 110,000 real-world decisions made by 1,000 individuals alongside choices generated by AI agents, the study found that large language models tend to predict average outcomes rather than unique or distinctive ones, potentially leading users towards safer, normative options. Matz advocates for incorporating an "exploration mode" in AI apps to preserve diversity and prevent cultural homogenization.
Written by the local model on 2026-09-17,
using this article's own text rather than the other coverage of the
same event (that is the story summary below).
Story summary
Columbia Business School professor Sandra Matz warns that artificial intelligence, particularly large language models (LLMs), might make people less creative and adventurous in their decisions. According to her study, LLMs often provide predictable and normative information based on average population trends rather than individual preferences. This analysis was based on more than 110,000 real-world decisions made by 1,000 people, alongside data from the myPersonality project, a Facebook application that conducted personality tests on users who shared their profiles for research purposes. Matz found that LLMs tend to nudge behavior towards more common choices and reduce the range of options explored by individuals, potentially diminishing cognitive diversity and innovation in society.
Written for “AI Impact On Human Skills” on 2026-09-17,
grounded in this article and the 0 other(s) covering the same event.
Why this leaning score
The model judged this article politically coded and scored it -0.65, but 1 quote(s) could not be found in the article and the other 3 are attributed speech rather than the article's own narration, so the score is not published.
Written under an earlier scoring contract, which gave a paragraph
rather than checkable quotes. Re-analysing this article replaces it.
Leaning score withheld for article 14533: no verified evidence · logged 2026-09-17
Artificial intelligence could one day supercharge human cognition, leading to significant advances in science, technology and other fields.
uncertain
intelligence → supercharge → science
It could also make us dull, new academic research suggests.
uncertain
research → make → ?
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.
asserted
that → call → 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.
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Matz → predict → 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.
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you → tell → wall
It homogenizes decisions, and we all get the same output."
To arrive at their findings, Matz and her study co-authors analyzed
asserted
Matz → homogenize → findings
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.
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who → use → purposes
"AI hates risk"
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AI → hat → 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.
uncertain
relying → rely → psychology
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.
asserted
Matz → narrow → what
"AI hates risk because we train it that way," Matz said.
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Matz → hat → it
"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.
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you → want → what
"
AI apps don't have to operate this way, but it's how they're programmed to work, she added.
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she → have → ?
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.
asserted
who → prevent → 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.
asserted
Matz → help → preferences
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