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AI could make people dull, one scientist fears. Here's why.

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Cerullo, M. (2026, September 16). AI could make people dull, one scientist fears. Here's why. CBS News. https://www.cbsnews.com/news/ai-making-people-dull-study/

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CBS News · collected 2026-09-16 · by Megan Cerullo

Quick Summary

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 AI on 2026-09-17, from this article's own text only (the story summary below draws on all the coverage).

AI analysis

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 2026-10-30, grounded in this article and the 0 other(s) covering the same event.

Story

📰 AI Impact On Human Skills
Technology · 1 article(s) covering the same event.

Compared with similar articles

This article reads unscored and hedges 20% of its claims. Each row says how that neighbour differs.
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Hedges 4 of 31 claims Outlet record 59/100
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The Dispatch
Hedges 1 of 2 claims Outlet record 61/100
“The articles discuss different aspects of AI concerns; Article A focuses on a scientist's fear that AI could make people dull, while Article B reports on President Trump's social media comments dismissing AI doomsday scenarios.”
The Dispatch
Hedges 5 of 32 claims Outlet record 61/100
“The articles discuss different aspects of AI impacts; one focuses on a researcher's resignation over existential risks, while the other discusses concerns about AI making people dull due to predictability.”
The Sydney Morning Herald
⚖️ Leans left Hedges 1 of 45 claims Outlet record 61/100
“The articles discuss related themes but describe different events: one is a scientific perspective on AI's potential to make people dull, while the other reports on public concerns about losing cognitive skills due to AI, based on survey results.”
How this article was measured signals, leaning charts, publisher record, byline, topics and claims

Signals How these are calculated →

Claims extracted
15
sentences that state something as happening or true
Uncertain
20%
3 of 15 hedged ("reportedly", "may", "according to")
Leaning
withheld
no quote in the article backed the model's score
Publisher signals
66/100
CBS News's corrections and hedging, across everything collected from it. Not a measure of accuracy.
Outlets on this story
1
Technology
Same framing elsewhere
1
articles that frame the event the way this one does
Analyzed 2026-09-17 · how these are computed

How this is being covered How these are calculated →

Article leaning vs. publisher reliability
Source leaning vs. consistency

Publisher

CBS News · 4181 article(s) · 6 correction(s) detected
Running correction rate · 6 correction(s)

Who wrote this

Megan Cerullo
28 article(s) here · 1 carrying a prediction
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Also by Megan Cerullo
Nothing else under this byline is closely related to this article, so these are simply their most recent.
All 28 articles by Megan Cerullo →

Topics

CBS News Columbia Business School Facebook Matz the myPersonality project

Subjects

Matz PERSON · 6× Facebook ORG · 2× CBS News ORG · 1× Columbia Business School ORG · 1× Sandra Matz PERSON · 1×

Narrative

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.
framing: assertive · carried by 1 article(s) · first seen 2026-09-17
🔮 Artificial intelligence could one day supercharge human cognition, leading to significant advances in science, technology and other fields.
2026-09-17 · CBS News
AI could make people dull, one scientist fears. Here's why. · assertive framing

Claims (15 extracted, 3 hedged)

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. asserted
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. asserted
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. asserted
who → use → purposes
"AI hates risk" asserted
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. asserted
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. asserted
you → want → what
" AI apps don't have to operate this way, but it's how they're programmed to work, she added. asserted
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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