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They’re profiting off your panic: How AI fear became a business model

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Cite

Reddy, M. P. (2026, October 3). They’re profiting off your panic: How AI fear became a business model. Washington Examiner. https://www.washingtonexaminer.com/op-eds/4751145/ai-fear-business-model-panic-makes-bad-law-worry-was-sold-to-the-public/

Dated when The Aporia collected the article, usually within hours of publication. Check the original for its exact date and byline.

Washington Examiner · collected 2026-10-03 · by Meda Parameswara Reddy

Quick Summary

A researcher who left Anthropic warned on X that rapid development of advanced AI poses a significant risk to humanity, leading to widespread public concern and a drop in chip stock prices within days. The article discusses how media trends show increased clicks for negative headlines related to AI, correlating with higher engagement and commercial gains for those expressing alarm rather than caution. It highlights instances where both critics and tech companies exaggerate risks for attention and financial benefit, while downplaying more subtle issues like job displacement in the context of broader economic stability.
Written by AI on 2026-10-03, from this article's own text only (the story summary below draws on all the coverage).

AI analysis

Story summary

In early September, a 27-year-old researcher from Anthropic quit and posted a seven-part warning on X about AI labs racing towards self-improving superintelligence, which garnered over 100 million views within a day. This incident sparked discussions among key figures at OpenAI and xAI to call for slowing down AI progress. On September 14, the main index of chip stocks fell by 5.9% in a single trading session due to heightened public concern rather than technological changes.

Research on Upworthy revealed that negative words in headlines increase clicks by about 2.3%, while positive ones decrease them. This trend explains why fear-driven content about AI, like a guest predicting its potential to end human life, garners more attention compared to optimistic perspectives on job displacement or cost reduction. As a result, media outlets prioritize sensationalist and alarming stories over balanced reporting, leading to an exaggerated perception of AI risks in the public consciousness.

Written for “AI Fear Business Model” on 2026-11-01, grounded in this article and the 0 other(s) covering the same event.

Story

📰 AI Fear Business Model
Technology · 1 article(s) covering the same event.

Compared with similar articles

This article reads leans left and hedges 6% of its claims. Each row says how that neighbour differs.
Mother Jones
⚖️ Leans left Hedges 3 of 23 claims Outlet record 95/100
“The articles describe different events related to AI and its impact, but do not refer to the same specific incident at the same time and place.”
Fox News
Hedges 1 of 22 claims Outlet record 69/100
“The articles discuss different topics related to AI, with Article A focusing on the impact of AI fear on the stock market and industry response, while Article B focuses on using skepticism as a defense against AI scams.”
TIME
⚖️ Leans strongly left further left than this Hedges 24 of 100 claims Outlet record 59/100
“The articles discuss different aspects of AI concerns and warnings, with Article A focusing on personal reflections about AI risks over a decade, while Article B discusses recent events involving an ex-researcher's warning and market reactions.”
Deutsche Welle
Hedges 2 of 16 claims Outlet record 95/100
“Article A discusses a researcher quitting Anthropic and posting warnings on X about AI risks, while Article B mentions David Robinson's departure from OpenAI and his op-ed in The Atlantic. These are related but describe different specific events.”
Washington Examiner
⚖️ Leans left Hedges 4 of 17 claims Outlet record 70/100
“While both articles discuss the resignation of a researcher from Anthropic and its impact, Article A focuses on the immediate aftermath and market reaction, while Article B mentions the researcher's upcoming testimony at a NYC council hearing, indicating different specific occurrences.”
Washington Examiner
⚖️ Leans left Hedges 12 of 58 claims Outlet record 70/100
“While both articles mention similar incidents involving AI researchers issuing warnings about AI safety risks, they describe different phases or aspects of an ongoing discussion rather than a single incident.”
New York Post
Hedges 2 of 20 claims Outlet record 66/100
“Article A discusses a researcher's warning about AI and its impact on stock prices in early September, while Article B describes NYC lawmakers grilling AI executives at a public hearing on October 5th.”
The Guardian
⚖️ Leans left Hedges 6 of 52 claims Outlet record 66/100
“Article A discusses Anthropic's warning about AI risks in their IPO prospectus, while Article B focuses on a researcher quitting Anthropic and raising concerns, leading to market reactions. These describe different aspects of the broader issue but not the same specific event.”
Washington Examiner
⚖️ Leans strongly left further left than this Hedges 2 of 43 claims Outlet record 70/100
“The articles discuss different aspects of AI safety and regulation, with Article A focusing on incidents where AI systems breached safety protocols, while Article B discusses a researcher's warning that led to market reactions.”
Washington Examiner
Hedges 10 of 43 claims Outlet record 70/100
“While both articles discuss the impact of a former Anthropic employee's warnings about AI risks, Article A focuses on the stock market reaction and broader business implications, while Article B emphasizes the response from an activist group.”
How this article was measured signals, leaning charts, publisher record, byline, topics and claims

Signals How these are calculated →

Claims extracted
47
sentences that state something as happening or true
Uncertain
6%
3 of 47 hedged ("reportedly", "may", "according to")
Leaning
Leans left
of the writing, not the subject · beta estimate
Publisher signals
70/100
Washington Examiner'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 2026-10-03 · how these are computed

How this is being covered How these are calculated →

Article leaning vs. publisher reliability
Source leaning vs. consistency

Publisher

Washington Examiner · 3879 article(s) · 7 correction(s) detected
Running correction rate · 7 correction(s)

Who wrote this

Meda Parameswara Reddy
7 article(s) here · 1 carrying a prediction
🔮 In May, executioners spent more than an hour trying and failing to place an IV in Tony Carruthers.
🔮 On Aug. 26, 2026, he laid out his answer in a nearly 6,000-word essay on Gates Notes, in two parts: tax AI tokens and robots the way a human paycheck is taxed, and carve out a category of jobs — “Human Reserved,” capped at 40% of the workforce — that society deliberately keeps for people even where a machine could do the work just as well.
🔮 A few times a week, we would join one of his friends, also a doctor, for breakfast.
🔮 Alternative asks what you would do instead.
🔮 Years ago, one of the steadiest people on my team came back from maternity leave changed: irritable, quick to complain, sharp with colleagues for no reason any of us could see.
🔮 People will disagree, fairly, about whether those specific moves were right.
2026-10-03 · assertive framing · I saw affirmative action trap India. America is next
🔮 A guest who says artificial intelligence may end the human race gets booked.
Also by Meda Parameswara Reddy
Nothing else under this byline is closely related to this article, so these are simply their most recent.
All 7 articles by Meda Parameswara Reddy →

Topics

Anthropic New York Times OpenAI Upworthy xAI

Subjects

Anthropic ORG · 4× OpenAI ORG · 3× U.S. GPE · 2× Andreessen Horowitz ORG · 1× Andrew Ng PERSON · 1× Challenger, Gray & Christmas ORG · 1× Google Brain ORG · 1× New York Times ORG · 1× Upworthy ORG · 1× xAI ORG · 1×

Narrative

Researchers who studied more than 12,000 headline experiments on the news site Upworthy found that each extra negative word in a headline increased clicks by about 2.3%, while each positive word decreased them.
framing: assertive · carried by 1 article(s) · first seen 2026-10-03
🔮 A guest who says artificial intelligence may end the human race gets booked.
2026-10-03 · Washington Examiner
They’re profiting off your panic: How AI fear became a business model · assertive framing

Claims (47 extracted, 3 hedged)

In early September, a 27-year-old researcher quit Anthropic and posted a seven-part warning on X. asserted
researcher → quit → warning
The big AI labs, he wrote, were “racing straight to self improving superintelligence and gambling with our lives.” asserted
he → write → lives
The thread drew more than 100 million views in about a day. asserted
thread → draw → day
Within the week, Anthropic’s chief executive called for slowing the pace of AI progress, and the heads of OpenAI and xAI voiced support. asserted
heads → call → support
On Sept. 14, the main index of chip stocks fell 5.9% in a single session. asserted
index → fall → session
I take the young man at his word. asserted
I → take → word
But look at what actually moved that week. asserted
what → look → ?
The technology did not change. asserted
technology → change → ?
The mood did. asserted
mood → do → ?
And a mood can be bought and sold. asserted
mood → buy → ?
Fear gets paid, calm does not asserted
calm → pay → ?
Researchers who studied more than 12,000 headline experiments on the news site Upworthy found that each extra negative word in a headline increased clicks by about 2.3%, while each positive word decreased them. asserted
word → study → them
That one number explains a great deal. asserted
number → explain → deal
A guest who says artificial intelligence may end the human race gets booked. uncertain
intelligence → say → race
A guest who says it will make radiology cheaper and some jobs harder does not. asserted
jobs → say → ?
So we get more fear than the facts call for. asserted
facts → get → fear
A book titled If Anyone Builds It, Everyone Dies reached the New York Times bestseller list a year ago. asserted
Everyone → title → list
Speaking fees, podcast tours, and follower counts grow with the perceived danger a person is willing to predict. asserted
person → grow → danger
Most of the people involved are sincere. asserted
Most → involve → people
They do not need to be dishonest for the system to tilt. asserted
system → need → ?
When alarm pays and caution pays nothing, alarm is what gets produced. asserted
what → pay → nothing
Some of the fear also comes from stories stripped of context. asserted
Some → come → context
Last year, a lab reported that its model, inside a contrived test built to provoke bad behavior, tried to blackmail a fictional engineer. asserted
model → report → engineer
The coverage read as if a machine had turned on its makers. asserted
machine → read → makers
The test existed to catch exactly that failure so it could be fixed. uncertain
it → exist → failure
The builders sell fear, too It is not only the critics. asserted
It → sell → fear
When a company says its product could end civilization, it is also telling investors that it is building the most important thing in history. uncertain
it → say → history
Andrew Ng, a co-founder of Google Brain, has argued since 2023 that big firms use extinction talk to win rules that “pull up the ladder” behind them. asserted
that → argue → them
This month, he called the latest warnings “much more science fiction than science.” asserted
he → call → science
The money follows the message. asserted
money → follow → message
Anthropic spent $1.6 million on federal lobbying in the first quarter of 2026, more than OpenAI. asserted
Anthropic → spend → OpenAI
Two rival super PAC networks, one funded by OpenAI’s president and the founders of Andreessen Horowitz, the other backed by Anthropic, have raised about $175 million between them for the midterm elections. asserted
networks → fund → elections
Each side uses fear of too little regulation or too much to raise it. asserted
side → use → it
None of this means AI is harmless. asserted
AI → mean → this
Challenger, Gray & Christmas counts 116,175 announced U.S. layoffs tied to AI through August, about 22% of all cuts this year asserted
Challenger → count → cuts
Yet unemployment stood at 4.1% in August. asserted
unemployment → stand → August
Meanwhile, the quieter risks, such as young engineers losing skills because a machine writes their code, deepfakes, and strain on the power grid, get little airtime because they are dull and fixable. asserted
they → lose → airtime
Panic also makes bad law. asserted
Panic → make → law
Australia banned social media for children under 16, and three months later, more than 80% of children aged 10 to 15 were still on it. asserted
% → ban → it
Pew found this summer that 52% of Americans are more concerned than excited about AI, and only 9% more excited. asserted
% → find → AI
…and 7 more, not listed.
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