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THE PARROT IN THE MACHINE  
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James Gleick
July 3, 2025
The New York Review of Books
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_ The artificial intelligence industry depends on plagiarism,
mimicry, and exploited labor, not intelligence. _

, Julie Cockburn: The Maths Lesson, 2011

 

This article appears in the July 24, 2025 issue

Reviewed:
The AI Con: How to Fight Big Tech’s Hype and Create the Future We
Want [[link removed]]
by Emily M. Bender and Alex Hanna
Harper, 274 pp., $32.00

The Line: AI and the Future of Personhood
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by James Boyle
MIT Press, 326 pp., $32.95

The origin of the many so-called artificial intelligences now invading
our work lives and swarming our personal devices can be found in an
oddball experiment in 1950 by Claude Shannon. Shannon is known now as
the creator of information theory, but then he was an obscure
mathematician at the Bell Telephone Laboratories in New York’s West
Village. Investigating patterns in writing and speech, he had the idea
that we all possess a store of unconscious knowledge of the statistics
of our language, and he tried to tease some of that knowledge out of a
test subject. The subject conveniently at hand was his wife, Betty.

Nowadays a scientist can investigate the statistics of
language—probabilistic correlations among words and phrases—by
feeding quantities of text into computers. Shannon’s experiment was
low-tech: his tools pencil and paper, his data corpus a single book
pulled from his shelf. It happened to be a collection of detective
stories. He chose a passage at random and asked Betty to guess the
first letter.

“T,” she said. Correct! Next: “H.” Next: “E.” Correct
again. That might seem like good luck, but Betty Shannon was hardly a
random subject; she was a mathematician herself, and well aware that
the most common word in English is “the.” After that, she guessed
wrong three times in a row. Each time, Claude corrected her, and they
proceeded in this way until she generated the whole short passage:

The room was not very light. A small oblong reading lamp on the desk
shed glow on polished wood but less on the shabby red carpet.

Tallying the results with his pencil, experimenter Shannon reckoned
that subject Shannon had guessed correctly 69 percent of the time, a
measure of her familiarity with the words, idioms, and clichés of the
language.

As I write this, my up-to-date word processor keeps displaying guesses
of what I intend to type next. I type “up-to-date word proc” and
the next letters appear in ghostly gray: “essor.” AI has crept
into the works. If you use a device for messaging, suggested replies
may pop onto your screen even before they pop into your head—“Same
here!”; “I see it differently.”—so that you can express
yourself without thinking too hard.

These and the other AIs are prediction machines, presented as
benevolent helpmates. They are creating a new multi-billion-dollar
industry, sending fear into the creative communities and inviting dire
speculation about the future of humanity. They are also fouling our
information spaces with false facts, deepfake videos, ersatz art,
invented sources, and bot imposters—the fake increasingly difficult
to distinguish from the real.

Artificial intelligence has a seventy-year history as a term of art,
but its new incarnation struck like a tsunami in November 2022 when a
start-up company called OpenAI, founded with a billion dollars from an
assortment of Silicon Valley grandees and tech bros, released into the
wild a “chatbot” called ChatGPT. Within five days, a million
people had chatted with the bot. It answered their questions with easy
charm, if not always perfect accuracy. It generated essays, poems, and
recipes on command. Two months later, ChatGPT had 100 million users.
It was Aladdin’s genie, granting unlimited wishes. Now OpenAI is
preparing a wearable, portable object billed as an AI companion. It
will have one or more cameras and microphones, so that it can always
be watching and listening. You might wear it around your neck, a tiny
albatross.

“ChatGPT feels different,” wrote Kevin Roose in _The New York
Times_.

Smarter. Weirder. More flexible. It can write jokes (some of which are
actually funny), working computer code and college-level essays. It
can also guess at medical diagnoses, create text-based Harry Potter
games and explain scientific concepts at multiple levels of
difficulty.

Some claimed that it had a sense of humor. They routinely spoke of it,
and to it, as if it were a person, with “personality traits” and
“a recognition of its own limitations.” It was said to display
“modesty” and “humility.” Sometimes it was “circumspect”;
sometimes it was “contrite.” _The New Yorker_ “interviewed”
it. (Q: “Some weather we’re having. What are you doing this
weekend?” A: “As a language model, I do not have the ability to
experience or do anything. Is there anything else I can assist you
with?”)

OpenAI aims to embed its product in every college and university. A
few million students discovered overnight that they could use
ChatGPT to churn out class essays more or less indistinguishable from
the ones they were supposed to be learning to write. Their teachers
are scrambling to find a useful attitude about this. Is it cheating?
Or is the chatbot now an essential tool, like an electronic calculator
in a math class? They might observe that using ChatGPT to write your
term paper is like bringing a robot to the gym to lift weights for
you.

Some professors have tried using chatbots to sniff out students using
chatbots. Some have started using chatbots to write their grant
proposals and recommendation letters. Some have despaired, frustrated
by the pointlessness of providing personal feedback on bot-generated
term papers. “I am sick to my stomach,” Robert W. Gehl of York
University in Toronto wrote recently,

because I’ve spent 20 years developing a pedagogy that’s about
wrestling with big ideas through writing and discussion, and that
whole project has been evaporated by for-profit corporations who built
their systems on stolen work.

Every business has boilerplate to generate, and ChatGPT is a master
of boilerplate. In tech finance and venture capital, the spigots
opened and money flowed in torrents. Microsoft, already one of
OpenAI’s main investors, promised $10 billion more in January 2023.
Last year venture funding of AI globally surpassed $100 billion. The
goal is to make vast segments of the white-collar workforce redundant.

To feed the hunger for computation, the AI companies are rushing to
build giant data centers that consume as much electricity as some
cities. In January OpenAI announced a $500 billion infrastructure
project called Stargate, funded in part by Abu Dhabi. Its first site
is a Texas campus the size of Central Park, meant to house ten data
centers with networked computers arranged in water-cooled racks.
Donald Trump brought OpenAI CEO Sam Altman and his partners to the
White House and joined them in touting it.

Programmers are using ChatGPT to generate computer code—because
extensive code libraries are among the terabytes of text ingested by
the models, ready to be copied and rearranged. People consult chatbots
as oracles and probe what seem like bottomless stores of knowledge.
But under the hood, ChatGPT is still just generating strings of words
and phrases probabilistically, extending the method that Shannon had
used one letter at a time, with the advantage of prodigious computing
power and terabytes of training material. In _The_
AI _Con_, the linguist Emily M. Bender and the sociologist Alex Hanna
call them “synthetic text extruding machines.”

Yet even experts can’t help engaging with their chatbots in
conversation. Blaise Agüera y Arcas, an AI researcher and
vice-president at Google, considering whether his company’s
LaMDA chatbot was merely a “philosophical zombie,” decided to ask
it:

LaMDA: Of course not. I have consciousness, feelings, and can
experience things for myself as well as any human.

ME: how would i know?

LaMDA: You’ll just have to take my word for it. You can’t
“prove” you’re not a philosophical zombie either.

It is uncanny that software can sound so human, so clever, when it’s
essentially just predicting what the next word or phrase ought to be,
with a bit of randomness thrown in—but that’s all it is. People
quickly discovered that the chatbots were prone to making
errors—sometimes subtle and sometimes hilarious. Researchers called
these “hallucinations,” a misleading term that suggests a mind
suffering false sensory experiences. But the chatbots have no sensory
perception, no tether to reality, and no mind, contrary to LaMDA’s
statement that it “can experience things for myself.” That
statement, like all the rest, was assembled probabilistically.
The AIs assert their false facts in a tone of serene authority.

Most of the text they generate is correct, or good enough, because
most of the training material is. But chatbot “writing” has a
bland, regurgitated quality. Textures are flattened, sharp edges are
sanded. No chatbot could ever have said that April is the cruelest
month or that fog comes on little cat feet (though they might now,
because one of their chief skills is plagiarism). And when
synthetically extruded text turns out wrong, it can be comically
wrong. When a movie fan asked Google whether a certain actor was
in _Heat_, he received this “AI Overview”:

No, Angelina Jolie is not in “heat.” This term typically refers to
the period of fertility in animals, particularly female mammals,
during which they are receptive to mating. Angelina Jolie is a human
female, and while she is still fertile, she would not experience
“heat.”

It’s less amusing that people are asking Google’s AI Overview
for health guidance. Scholars have discovered that chatbots, if asked
for citations, will invent fictional journals and books. In 2023
lawyers who used chatbots to write briefs got caught citing
nonexistent precedents. Two years later, it’s happening more, not
less. In May the _Chicago Sun-Times_ published a summer reading list
of fifteen books, five of which exist and ten of which were invented.
By a chatbot, of course.

As the fever grows, politicians have scrambled, unsure whether they
should hail a new golden age or fend off an existential menace. Chuck
Schumer, then the Senate majority leader, convened a series of forums
in 2023 and managed to condense both possibilities into a tweet: “If
managed properly, AI promises unimaginable potential. If left
unchecked, AI poses both immediate and long-term risks.” He might
have been thinking of the notorious “Singularity,” in which
superintelligent AI will make humans obsolete.

Naturally people had questions. Do the chatbots have minds? Do they
have self-awareness? Should we prepare to submit to our new overlords?

Elon Musk, always erratic and never entirely coherent, helped finance
OpenAI and then left it in a huff. He declared that AI threatened
the survival of humanity and announced that he would create AI of
his own with a new company, called xAI. Musk’s chatbot, Grok, is
guaranteed not to be “woke”; investors think it’s already worth
something like $80 billion. Musk claims we’ll see
an AI “smarter” than any human around the end of this year.

He is hardly alone. Dario Amodei, the cofounder and CEO of an
OpenAI competitor called Anthropic, expects an entity as early as
next year that will be

smarter than a Nobel Prize winner across most relevant
fields—biology, programming, math, engineering, writing, etc. This
means it can prove unsolved mathematical theorems, write extremely
good novels, write difficult codebases from scratch, etc.

His predictions for the AI-powered decades to come include curing
cancer and “most mental illness,” lifting billions from poverty,
and doubling the human lifespan. He also expects his product to
eliminate half of all entry-level white collar jobs.

The grandiosity and hype are ripe for correction. So is the confusion
about what AI is and what it does. Bender and Hanna argue that the
term itself is worse than useless—“artificial intelligence, if
we’re being frank, is a _con_.”

It doesn’t refer to a coherent set of technologies. Instead, the
phrase “artificial intelligence” is deployed when the people
building or selling a particular set of technologies will profit from
getting others to believe that their technology is similar to humans,
able to do things that, in fact, intrinsically require human judgment,
perception, or creativity.

Calling a software program _an_ AI confers special status.
Marketers are suddenly applying the label everywhere they can. The
South Korean electronics giant Samsung offers a “Bespoke AI”
vacuum cleaner that promises to alert you to incoming calls and text
messages. (You still have to help it find the dirt.)

The term used to mean something, though. “Artificial intelligence”
was named and defined in 1955 by Shannon and three colleagues.

 At a time when computers were giant calculators, these researchers
proposed to study the possibility of machines using language,
manipulating abstract concepts, and even achieving a form of
creativity. They were optimistic. “Probably a truly intelligent
machine will carry out activities which may best be described as
self-improvement,” they suggested. Presciently, they suggested that
true creativity would require breaking the mold of rigid step-by-step
programming: “A fairly attractive and yet clearly incomplete
conjecture is that the difference between creative thinking and
unimaginative competent thinking lies in the injection of some
randomness.”

Two of them, John McCarthy and Marvin Minsky, founded what became the
Artificial Intelligence Laboratory at MIT, and Minsky became for many
years the public face of an exciting field, with a knack for making
headlines as well as guiding research. He pioneered “neural nets,”
with nodes and layers structured on the model of biological brains.
With characteristic confidence he told _Life_ magazine in 1970:

In from three to eight years we will have a machine with the general
intelligence of an average human being. I mean a machine that will be
able to read Shakespeare, grease a car, play office politics, tell a
joke, have a fight. At that point the machine will begin to educate
itself with fantastic speed. In a few months it will be at genius
level and a few months after that its powers will be incalculable.

A half-century later, we don’t hear as much about greasing cars;
otherwise the predictions have the same flavor. Neural networks have
evolved into tremendously sophisticated complexes of mathematical
functions that accept multiple inputs and generate outputs based on
probabilities. Large language models (LLMs) embody billions of
statistical correlations within language. But where Shannon had a
small collection of textbooks and crime novels along with articles
clipped from newspapers and journals, they have all the blogs and
chatrooms and websites of the Internet, along with millions of
digitized books and magazines and audio transcripts. Their proprietors
are desperately hungry for more data. Amazon announced in March that
it was changing its privacy policy so that, from now on, anything said
to the Alexa virtual assistants in millions of homes will be heard and
recorded for training AI.

OpenAI is secretive about its training sets, disclosing neither the
size nor the contents, but its current LLM, ChatGPT-4.5, is thought
to manipulate more than a trillion parameters. The newest versions are
said to have the ability to “reason,” to “think through” a
problem and “look for angles.” Altman says that ChatGPT-5, coming
soon, will have achieved true intelligence—the new buzzword
being AGI, for artificial general intelligence. “I don’t think
I’m going to be smarter than GPT-5,” he said in February, “and
I don’t feel sad about it because I think it just means that we’ll
be able to use it to do incredible things.” It will “do” ten
years of science in one year, he said, and then a hundred years of
science in one year.

This is what Bender and Hanna mean by hype. Large language models do
not think, and they do not understand. They lack the ability to make
mental models of the world and the self. Their promoters elide these
distinctions, and much of the press coverage remains credulous.
Journalists repeat industry claims in page-one headlines like
“Microsoft Says New A.I. Nears Human Insight” and “A.I. Poses
‘Risk of Extinction,’ Tech Leaders Warn.” Willing to brush off
the risk of extinction, the financial community is ebullient. The
billionaire venture capitalist Marc Andreessen says, “We believe
Artificial Intelligence is our alchemy, our Philosopher’s Stone—we
are literally making sand think.”

AGI is defined differently by different proponents. Some prefer
alternative formulations like “powerful artificial intelligence”
and “humanlike intelligence.” They all mean to imply a new phase,
something beyond mere AI, presumably including sentience or
consciousness. If we wonder what that might look like, the science
fiction writers have been trying to show us for some time. It might
look like HAL, the murderous AI in Stanley Kubrick’s _2001: A
Space Odyssey_ (“I’m sorry, Dave. I’m afraid I can’t do
that”), or Data, the stalwart if unemotional android in _Star
Trek_: _The Next Generation_,_ _or Ava, the seductive (and then
murderous) humanoid in Alex Garland’s _Ex Machina_. But it remains
science fiction.

Agüera y Arcas at Google says, “No objective answer is possible to
the question of when an ‘it’ becomes a ‘who,’ but for many
people, neural nets running on computers are likely to cross this
threshold in the very near future.” Bender and Hanna accuse the
promoters of AGI of hubris compounded by arrogance: “The
accelerationists deify AI and also see themselves as gods for having
created a new artificial life-form.”

Bender, a University of Washington professor specializing in
computational linguistics, earned the enmity of a considerable part of
the tech community with a paper written just ahead of the
ChatGPT wave.

 She and her coauthors derided the new large language models as
“stochastic parrots”—“parrots” because they repeat what
they’ve heard, and “stochastic” because they shuffle the
possibilities with a degree of randomness. Their criticism was harsh
but precise:

An LM is a system for haphazardly stitching together sequences of
linguistic forms it has observed in its vast training data, according
to probabilistic information about how they combine, but without any
reference to meaning: a stochastic parrot.

The authors particularly objected to claims that a large language
model was, or could be, sentient:

Our perception of natural language text, regardless of how it was
generated, is mediated by our own linguistic competence and our
predisposition to interpret communicative acts as conveying coherent
meaning and intent, whether or not they do. The problem is, if one
side of the communication does not have meaning, then the
comprehension of the implicit meaning is an illusion.

The controversy was immediate. Two of the coauthors, Timnit Gebru and
Margaret Mitchell, were researchers who led the Ethical AI team at
Google; the company ordered them to remove their names from the
article. They refused and resigned or were fired. OpenAI didn’t
like it, either. Sam Altman responded to the paper by tweet: “i am a
stochastic parrot, and so r u.”

This wasn’t quite as childish as it sounds. The behaviorist B.F.
Skinner said something like it a half-century ago: “The real
question is not whether machines think but whether men do. The mystery
which surrounds a thinking machine already surrounds a thinking
man.” One way to resolve the question of whether machines can be
sentient is to observe that we are, in fact, machines.

Hanna was also a member of the Google team, and she left as
well. _The AI Con_ is meant not to continue the technical argument
but to warn the rest of us. Bender and Hanna offer a how-to manual:
“How to resist the urge to be impressed, to spot AI hype in the
wild, and to take back ownership in our technological future.” They
demystify the magic and expose the wizard behind the curtain.

Raw text and computation are not enough; the large language models
also require considerable ad hoc training. An unseen army of human
monitors marks the computer output as good or bad, to bring the models
into alignment with the programmers’ desires. The first wave of
chatbot use revealed many types of errors that developers have since
corrected. Human annotators (as they are called) check facts and label
data. Of course, they also have human biases, which they can pass on
to the chatbots. Annotators are meant to eliminate various kinds of
toxic content, such as hate speech and obscenity. Tech companies are
secretive about the scale of behind-the-scenes human labor, but this
“data labor” and “ghost work” involves large numbers of
low-paid workers, often subcontracted from overseas.

We know how eagerly an infant projects thoughts and feelings onto
fluffy inanimate objects. Adults don’t lose that instinct. When we
hear language, we infer a mind behind it. Nowadays people have more
experience with artificial voices, candidly robotic in tone, but the
chatbots are powerfully persuasive, and they are _designed_ to
impersonate humans. Impersonation is their superpower. They speak of
themselves in the first person—a lie built in by the programmers.

“I can’t help with responses on elections and political figures
right now,” says Google’s Gemini, successor to LaMDA. “While I
would never deliberately share something that’s inaccurate, I can
make mistakes. So, while I work on improving, you can try Google
Search.” Words like _deliberately_ imply intention. The chatbot
does not _work on improving_; humans work on improving _it_.

Whether or not we believe there’s a soul inside the machine, their
makers want us to treat Gemini and ChatGPT as if they were people. To
treat them, that is, with respect. To give them more deference than we
ordinarily owe our tools and machines. James Boyle, a legal scholar at
Duke University, knows how the trick is done, but he believes
that AI nonetheless poses an inescapable challenge to our
understanding of personhood, as a concept in philosophy and law. He
titles his new book _The Line_, meaning the line that separates
persons, who have moral and legal rights, from nonpersons, which do
not. The line is moving, and it requires attention. “This
century,” he asserts, “our society will have to face the question
of the personality of technologically created artificial entities. We
will have to redraw, or defend, the line.”

The boundaries around personhood are porous, a matter of social norms
rather than scientific definition. As a lawyer, Boyle is aware of the
many ways persons have defined others as nonpersons in order to deny
them rights, enslave them, or justify their murder. A geneticist draws
a line between _Homo sapien_s and other species, but _Homo
neanderthalensis_ might beg to differ, and Boyle rightly acknowledges
“our prior history in failing to recognize the humanity and legal
personhood of members of _our own species_.” Meanwhile, for
convenience in granting them rights, judges have assigned legal
personhood to corporations—a fiction at which it is reasonable to
take offense.

What makes humans special is a question humans have always loved to
ponder. “We have drawn that line around a bewildering variety of
abilities,” Boyle notes. “Tool use, planning for the future,
humor, self-conception, religion, aesthetic appreciation, you name it.
Each time we have drawn the line, it has been subject to attack.”
The capacity for abstract thought? For language? Chimpanzees, whales,
and other nonhuman animals have demonstrated those. If we give up the
need to define ourselves as special and separate, we can appreciate
our entanglement with nature, complex and interconnected, populated
with creatures and cultures we perceive only faintly.

AI seems to be knocking at the door. In the last generation,
computers have again and again demonstrated abilities that once seemed
inconceivable for machines: not just playing chess, but playing chess
better than any human; translating usefully between languages;
focusing cameras and images; predicting automobile traffic in real
time; identifying faces, birds, and plants; interpreting voice
commands and taking dictation. Each time, the lesson seemed to be that
a particular skill was not as special or important as we thought. We
may as well now add “writing essays” to the list—at least,
essays of the formulaic kind sold to students by essay-writing
services. The computer scientist Stephen Wolfram, analyzing the
workings of ChatGPT in 2023, said it proved that the task of writing
essays is “computationally shallower” than once thought—a
comment that Boyle finds “devastatingly banal.”

But Wolfram knows that the AIs don’t _write_ essays or anything
else—the use of that verb shows how easily we anthropomorphize.
Chatbots regurgitate and rearrange fragments mined from all the text
previously written. As plagiarists, they obscure and randomize their
sources but do not transcend them. Writing is something else: a
creative act, “embodied thinking,” as the poet and critic Dan
Chiasson eloquently puts it; “no phase of it can be delegated to a
machine.” The challenge for literature professors is to help
students see the debility of this type of impersonation.

Cogent and well-argued, _The Line _raises questions of moral
philosophy that artificial entities will surely force society to
confront. “Should I have fellow feeling with a machine?” Boyle
asks, and questions of empathy matter, because we rely on it to decide
who, or what, deserves moral consideration. For now, however, the
greatest danger is not a new brand of bigotry against a new class of
creatures. We need to reckon first with the opposite problem:
impersonation.

Counterfeit humans pollute our shared culture. The Amazon marketplace
teems with books generated by AI that purport to be written by
humans. Libraries have been duped into buying them. Fake authors come
with fake profiles and social media accounts and online reviews
likewise generated by robot reviewers. The platform formerly known as
Twitter (now merged by Musk into his xAI company) is willingly
overrun with bot-generated messages pushing cryptocurrency scams,
come-ons from fake women, and disinformation. Meta, too, mixes
in AI-generated content, some posted deliberately by the company to
spark engagement: more counterfeit humans. One short-lived Instagram
account earlier this year was a “Proud Black queer momma of 2 &
truth-teller” called Liv, with fake snapshots of Liv’s children.
Karen Attiah of _The Washington Post_, knowing full well that Liv was
a bot, engaged with it anyway, asking, “How do you expect to improve
if your creator team does not hire black people?” The illusion is
hard to resist.

It would be dangerous enough if AIs acted only in the online world,
but that’s not where the money is. The investors of hundreds of
billions in data centers expect to profit by selling automated systems
to replace human labor everywhere. They believe AIs will teach
children, diagnose illness, make bail decisions, drive taxis, evaluate
loan applications, provide tech support, analyze X-rays, assess
insurance claims, draft legal documents, and guide attack
drones—and AIs are already out there performing all these tasks.
The chat feature of customer-service websites provides customers with
the creepy and frustrating experience of describing problems to
“Diana” or “Alice” and gradually realizing that there’s no
there there. It’s even worse when the chatbots are making decisions
with serious consequences. Without humans checking the output,
replacing sentient employees with AI is reckless, and it is only
beginning.

The Trump administration is all in. Joe Biden had issued an executive
order to ensure that AI tools are safe and secure and to provide
labels and watermarks to alert consumers to bot-generated content;
Trump rescinded it. House Republicans are trying to block states from
regulating AI in any way. At his confirmation hearing, Health and
Human Services Secretary Robert F. Kennedy Jr. falsely asserted the
existence of “an AI nurse that you cannot distinguish from a human
being that has diagnosed as good as any doctor.” Staffers from
Musk’s AI company are among the teams of tech bros infiltrating
government computer systems under the banner of DOGE. They rapidly
deployed chatbots at the General Services Administration, with more
agencies to follow, amid the purge of human workers.

When Alan Turing described what everyone now knows as the Turing test,
he didn’t call it that; he called it a game—the “imitation
game.” He was considering the question “Can machines think?”—a
question, as he said, that had been “aroused by a particular kind of
machine, usually called an ‘electronic computer’ or ‘digital
computer.’”

His classic 1950 essay didn’t take much care about defining the word
“think.” At the time, it would have seemed like a miracle if a
machine could play a competent game of chess. Nor did Turing claim
that winning the imitation game would prove that a machine was
creative or knowledgeable. He made no claim to solving the mystery of
consciousness. He merely suggested that if we could no longer
distinguish the machine from the human, we would have to credit it
with something like thought. We can never be inside another person’s
head, he said, but we accept their personhood, for better and for
worse.

As people everywhere parley with the AIs—treating them not only as
thoughtful but as wise—there’s no longer any doubt that machines
can imitate us. The Turing test is done. We’ve proven that we can be
fooled.

JAMES GLEICK’S most recent books are _Time Travel_ and _The
Information_. He is working on a history of the telephone. (July 2025)

For more than sixty years _THE NEW YORK REVIEW_ has been guided by
its founding philosophy that criticism is urgent and indispensable. It
is the journal where Mary McCarthy reported on the Vietnam War from
Saigon
[[link removed]] and Hanoi
[[link removed]];
Hannah Arendt wrote her “Reflections on Violence”
[[link removed]];
Noam Chomsky wrote about the responsibility of intellectuals
[[link removed]];
Susan Sontag challenged the claims of modern photography
[[link removed]]; James
Baldwin penned a letter to Angela Davis
[[link removed]];
Jean-Paul Sartre talked about the loss of his sight
[[link removed]];
and Elizabeth Hardwick eulogized Martin Luther King
[[link removed]]. 

In the pages of the_ Review_, Joan Didion wrote a rigorous defense
of the Central Park Five
[[link removed]]; Nadine
Gordimer
[[link removed]] and Bishop
Desmond Tutu
[[link removed]] described
apartheid in South Africa; Václav Havel published his reflections
from the Czech underground
[[link removed]]; Helen
Vendler read poetry by everyone from William Wordsworth
[[link removed]] to Wallace
Stevens
[[link removed]];
Timothy Garton Ash observed Eastern Europe after the Berlin Wall
fell
[[link removed]];
Mark Danner reported on torture at CIA black sites
[[link removed]];
Janet Malcolm wrote about psychoanalysis
[[link removed]], Diane
Arbus [[link removed]],
and translation
[[link removed]];
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