Is “creativity”
meaningless?
Asteroidblocking nukes
Building cities
with lava
Vol. 128
No. 3
May/June 2025
Muse or machine?
Defining creativity in the age of AI
Display until July 1, 2025
Creativity
MJ25-front_cover.indd 1
4/2/25 1:1 PM
Sponsored Content
The Agentic AI Advantage:
Unlocking the next level of
enterprise value
I
ntelligence versus agency.
That’s the distinction between
generative AI—which has already
created tremendous disruption and
opportunity across businesses—
and agentic AI, its next evolution,
says Swami Chandrasekaran,
Global Head of AI and Data Labs at
KPMG LLP.
AI agents are powerful, controlled
autonomous software entities that
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They leverage the capabilities of
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How can AI agents unlock
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AI agents can unlock greater
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1
fundamentally transform how work
gets accomplished through their
collectively reinforced intelligence.
By working seamlessly with
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analysis of 17 million companies
and 3 billion data points.1
Agentic AI will unlock even
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embedding AI more easily into
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-Per Edin, AI Go-to-Market
Leader – Global Advisory and US
Tech, Media and Telecom Sector,
KPMG LLP
The KPMG TACO Framework™
To help organizations navigate
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Orchestrators are the most
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enable resource optimization
at unprecedented scale.”
-Swami Chandrasekaran, Global
Head of AI and Data Labs at
KPMG LLP
The art of moving quickly:
Address challenges and
concerns early
To move at market speed in
adopting agentic AI, organizations
must address several challenges
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1. Decide how to acquire agents
Decide whether to build,
buy, or partner to acquire AI
agents, considering the needs,
resources, and strategic goals
of the organization. Building
AI agents in-house allows for
customization and control but
requires deep expertise in AI,
data science, and software
Quantifying the GenAI opportunity, March 2025, KPMG U.S., https://kpmg.com/us/en/articles/2025/quantifying-the-genai-opportunity.html
© 2025 KPMG LLP, a Delaware limited liability partnership and a member firm of the KPMG global organization of independent member
firms affiliated with KPMG International Limited, a private English company limited by guarantee. USCS028154-1A
Untitled-6 1
3/31/25 1 :11 M
Sponsored Content
development. Buying pre-built
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3. Consider the workforce
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Build a skilled workforce
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How do we get started? An
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The integration of agentic AI
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Expand existing AI governance
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Continuously measure and
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Unlocking the full potential of
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The transition to agentic AI
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By embracing the proactive and
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positioned for the transformative
impact of more sophisticated AI
agents that are on the horizon.
These will create not only longerterm value in operations, but
also entirely reinvented business
models and industries.
**********
Per Edin is the AI Go-To-Market Leader
for KPMG Global Advisory and US Tech,
Media and Telecom Sector, KPMG
LLP. He is responsible for aligning
AI client offerings, messaging, and
go-to-market strategies across the
global network. His work includes
leading the firm's initiatives to help
clients navigate generative AI market
disruption, launching the firm's Advisory
AI Services portfolio, and developing
assets such as the KPMG GenAI value
assessment.
Swami Chandrasekaran is Global
Head of AI and Data Labs at KPMG
LLP. He leads and executes the firm’s
AI strategy across Tax, Audit, Advisory,
and other functions, serving 200,000
knowledge professionals worldwide.
He directs and oversees various
R&D efforts and initiatives covering
AI architecture, advanced knowledge
assistants, AI Agents, domain-tuned
Small Language Models (SLMs),
synthetic data, enterprise discovery
and search, and hardware-optimized
solutions, while also chairing KPMG's
AI Technology Review Board to ensure
trusted and scalable AI adoption.
**********
KPMG is here to guide you with an
AI strategy that drives value and
sparks innovation. Learn more at
www.kpmg.us/ai.
© 2025 KPMG LLP, a Delaware limited liability partnership and a member firm of the KPMG global organization of independent member
firms affiliated with KPMG International Limited, a private English company limited by guarantee. USCS028154-1A
Untitled-7 1
3/31/25 1 :12 M
02
From the editor
T
MJ25-front_editorial.indd 2
Mat Honan
is editor in
chief of
MIT Technology
Review.
Carrie Klein, Carly Kay, Matthew Ponsford, and Robin George
Andrews. (If you’ve ever wanted to know how we might nuke
an asteroid, this is the issue for you!)
We’re also trying to get a little more creative ourselves. Over
the next few issues, you’ll notice some changes coming to this
magazine with the addition of some new regular items (see
Caiwei Chen’s “3 Things” for one such example). Among those
changes, we are planning to solicit and publish more regular
reader feedback and answer questions you may have about technology. We invite you to get creative and email us: newsroom@
technologyreview.com.
As always, thanks for reading.
Mat Honan
ROBYN KESSLER
he reason you are reading this letter from me
today is that I was bored 30 years ago.
I was bored and curious about the world
and so I wound up spending a lot of time in the
university computer lab, screwing around on
Usenet and the early World Wide Web, looking for interesting things to read. Soon enough
I wasn’t content to just read stuff on the internet—I wanted to
make it. So I learned HTML and made a basic web page, and
then a better web page, and then a whole website full of web
things. And then I just kept going from there. That amateurish collection of web pages led to a journalism internship with
the online arm of a magazine that paid little attention to what
we geeks were doing on the web. And that led to my first real
journalism job, and then another, and, well, eventually this
journalism job.
But none of that would have been possible if I hadn’t been
bored and curious. And more to the point: curious about tech.
The university computer lab may seem at first like an unlikely
center for creativity. We tend to think of creativity as happening
more in the artist’s studio or writers’ workshop. But throughout
history, very often our greatest creative leaps—and I would argue
that the web and its descendants represent one such leap—have
been due to advances in technology.
There are the big easy examples, like photography or the
printing press, but it’s also true of all sorts of creative inventions
that we often take for granted. Oil paints. Theaters. Musical
scores. Electric synthesizers! Almost anywhere you look in
the arts, perhaps outside of pure vocalization, technology has
played a role.
But the key to artistic achievement has never been the technology itself. It has been the way artists have applied it to express
our humanity. Think of the way we talk about the arts. We often
compliment it with words that refer to our humanity, like soul,
heart, and life; we often criticize it with descriptors such as sterile,
clinical, or lifeless. (And sure, you can love a sterile piece of art,
but typically that’s because the artist has leaned into sterility to
make a point about humanity!)
All of which is to say I think that AI can be, will be, and already
is a tool for creative expression, but that true art will always be
something steered by human creativity, not machines.
I could be wrong. I hope not.
This issue, which was entirely produced by human beings
using computers, explores creativity and the tension between
the artist and technology. You can see it on our cover illustrated
by Tom Humberstone, and read about it in stories from James
O’Donnell, Will Douglas Heaven, Rebecca Ackermann, Michelle
Kim, Bryan Gardiner, and Allison Arieff.
Yet of course, creativity is about more than just the arts. All
of human advancement stems from creativity, because creativity is how we solve problems. So it was important to us to bring
you accounts of that as well. You’ll find those in stories from
4/1/25 12:2 PM
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Untitled-4 1
12/ /24 1:2 PM
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3/31/25 3:1 PM
Contents
05
“How can we make art without friction?
How can we engage in a truly creative process
without material that pushes back?” –p. 24
Front
2
Letter from the editor
THE DOWNLOAD
7
Bird-flu-detecting sensors; hot tubs heated
by bitcoin; the surprising urgency of AM radio;
air traffic control for drones; islands built with
ocean currents; a new play about OpenAI;
and the TR Bookshelf
PROFILE
18 The new hotness
An Icelandic architect wants to build cities
out of lava. By Elissaveta M. Brandon
Back
58 The story of the Armatron
How a 1980s toy robot arm inspired modern
robotics. By Jon Keegan
64 Fighting drug overdoses with science
Could NIST’s early warning system for new
adulterants in street drugs help save lives?
By Adam Bluestein
The creativity issue
24 How AI can help supercharge creativity
The artists and musicians finding ways to make art
with generative models’ help. BY WILL DOUGLAS HEAVEN
32 Creative differences
Q&A: In The Cult of Creativity, Samuel Franklin
excavates the surprisingly recent history of an idea,
an ideal, and an ideology. BY BRYAN GARDINER
36 Replication and creation
New diffusion AI models that make music are
complicating our definitions of human creativity.
BY JAMES O ’ DONNELL
68 Generative AI is reshaping South Korea’s
web comics industry
It’s unlocking new creative possibilities
while fueling anxieties over agency and
authorship. By Michelle Kim
74 Book review: The AI is present
Three books examine what we gain
and lose when we let machines create.
By Rebecca Ackermann
80 Discoveries and failures
New fiction by Alexandra Chang
88 The AI Hype Index
Our highly subjective take on the latest
buzz in artificial intelligence.
42 Atoms for asteroids
A nuclear explosion might eventually be Earth’s only
way to protect itself from a dangerous space rock. But
preparing for that day without testing nukes in space
means getting creative. BY ROBIN GEORGE ANDREWS
50 Generating architecture
Artificial intelligence is painting pictures, writing
novels, making videos, and composing symphonies.
Can it push the limits of what we build?
BY ALLISON ARIEFF
MJ25-front_contents.indd 5
Cover by
Tom
Humberstone
4/2/25 1:13 PM
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Untitled-1 1
4/2/25 1 :3
M
07
The
Download
BY CARLY KAY
ERIC PALMA
This biosensor
detects bird flu
in five minutes
Researchers at
Washington University
developed a device
that may help prevent
future outbreaks of the
disease.
MJ25-front_thedownload.indd 7
Over the winter, eggs suddenly became all but impossible to buy.
As a bird flu outbreak rippled through dairy and poultry farms,
grocery stores struggled to keep them on shelves. The shortages
and record-high prices in February raised costs dramatically for
restaurants and bakeries and led some shoppers to skip the breakfast staple entirely. But a team based at Washington University
in St. Louis has developed a device that could help slow future
outbreaks by detecting bird flu in air samples in just five minutes.
Bird flu is an airborne virus that spreads between birds
and other animals. Outbreaks on poultry and dairy farms are
devastating; mass culling of exposed animals can be the only
way to stem outbreaks. Some bird flu strains have also infected
humans, though this is rare. As of early March, there had been
70 human cases and one confirmed death in the US, according
to the Centers for Disease Control and Prevention.
The most common way to detect bird flu involves swabbing
potentially contaminated sites and sequencing the DNA that’s
been collected, a process that can take up to 48 hours.
4/2/25 2:1 PM
08
The Download
BY CARRIE KLEIN
Data
In mid-March, the US Centers for Disease
Control and Prevention said there had been
70 confirmed human cases of avian influenza
A(H5) in the US since April 2024, linking 26
to exposure to infected poultry.
By that time, the US Department of Agriculture estimated, A(H5) had affected more
than 90 million birds, from both commercial
and backyard flocks.
The CDC said the immediate risk to the
general public from the virus was low.
Carly Kay is a science writer based in Santa Cruz,
California.
MJ25-front_thedownload.indd 8
Coming to a
spa near you:
Heat by bitcoin
Bitcoin enthusiasts are using a side effect of
mining’s immense computational load to heat
hot tubs, office buildings, and homes.
At first glance, the Bathhouse
spa in Brooklyn looks not so
different from other highend spas. What sets it apart
is out of sight: a closet full of
cryptocurrency-mining computers that not only generate
bitcoins but also heat the spa’s
pools, marble hammams, and
showers.
When cofounder Jason
Goodman opened Bathhouse’s
first location in Williamsburg in
2019, he used conventional pool
heaters. But after diving deep
into the world of bitcoin, he
realized he could fit cryptocurrency mining seamlessly into his
business. That’s because the
process, where special computers (called miners) make
trillions of guesses per second
to try to land on the string of
numbers that will earn a bitcoin, consumes tremendous
amounts of electricity—which
in turn produces plenty of heat
that usually goes to waste.
“I thought, ‘That’s interesting—we need heat,’” Goodman
says of Bathhouse. Mining facilities typically use fans or water
to cool their computers. And
pools of water, of course, are a
prominent feature of the spa.
It takes six miners, each
roughly the size of an Xbox One
console, to maintain a hot
tub at 104 °F. At Bathhouse’s
Williamsburg location, miners
hum away quietly inside two
large tanks, tucked in a storage
closet among liquor bottles and
teas. To keep them cool and
quiet, the units are immersed
directly in non-conductive oil,
which absorbs the heat they
give off and is pumped through
tubes beneath Bathhouse’s hot
tubs and hammams.
Mining boilers, which cool
the computers by pumping in
cold water that comes back
out at 170 °F, are now also
being used at the site. A thermal battery stores excess heat
for future use.
Goodman says his spas
aren’t saving energy by using
bitcoin miners for heat, but
they’re also not using any more
than they would with conventional water heating. “I’m
just inserting miners into that
chain,” he says.
Goodman isn’t the only one
to see the potential in heating with crypto. In Finland,
Marathon Digital Holdings
turned fleets of bitcoin miners
into a district heating system
to warm the homes of 80,000
residents. HeatCore, an integrated energy service provider,
DATA SOURCE: US CENTERS FOR DISEASE CONTROL AND PREVENTION
The new device samples the air in real time, running the samples past a specialized biosensor every
five minutes. The sensor has strands of genetic material called aptamers that were used to bind specifically to the virus. When that happens, it creates a
detectable electrical change. The research, published
in ACS Sensors in February, may help farmers contain
future outbreaks.
Part of the group’s work was devising a way to
deliver airborne virus particles to the sensor.
With bird flu, says Rajan Chakrabarty, a professor
of energy, environmental, and chemical engineering at Washington University and lead author of the
paper, “the bad apple is surrounded by a million or
a billion good apples.” He adds, “The challenge was
to take an airborne pathogen and get it into a liquid
form to sample.”
The team accomplished this by designing a
microwave-size box that sucks in large volumes of air
and spins it in a cyclone-like motion so that particles
stick to liquid-coated walls. The process seamlessly
produces a liquid drip that is pumped to the highly
sensitive biosensor.
Though the system is promising, its effectiveness in
real-world conditions remains uncertain, says Sungjun
Park, an associate professor of electrical and computer
engineering at Ajou University in South Korea, who
was not involved in the study. Dirt and other particles
in farm air could hinder its performance. “The study
does not extensively discuss the device’s performance
in complex real-world air samples,” Park says.
But Chakrabarty is optimistic that it will be commercially viable after further testing and is already
working with a biotech company to scale it up. He
hopes to develop a biosensor chip that detects multiple pathogens at once. ■
4/2/25 2:1 PM
JENNY KROIK
The Download
has used bitcoin mining to heat
a commercial office building in
China and to keep pools at a
constant temperature for fish
farming. This year it will begin
a pilot project to heat seawater for desalination. On a
smaller scale, bitcoin fans who
also want some extra warmth
can buy miners that double as
space heaters.
Crypto enthusiasts like
Goodman think much more
of this is coming—especially
under the Trump administration, which has announced
plans to create a bitcoin
reserve. This prospect alarms
environmentalists.
The energy required for
a single bitcoin transaction
varies, but as of mid-March it
was equivalent to the energy
consumed by an average US
household over 47.2 days,
according to the Bitcoin
Energy Consumption Index, run
by the economist Alex de Vries.
Among the various cryptocurrencies, bitcoin mining gobbles up the most energy by far.
De Vries points out that others, like ethereum, have eliminated mining and implemented
less energy-intensive algorithms. But bitcoin users resist
any change to their currency,
so de Vries is doubtful a shift
away from mining will happen
anytime soon.
One key barrier to using
bitcoin for heating, de Vries
says, is that the heat can only
be transported short distances
before it dissipates. “I see this
as something that is extremely
niche,” he says. “It’s just not
competitive, and you can’t
make it work at a large scale.”
The more renewable
sources that are added to
MJ25-front_thedownload.indd 9
electric grids to replace fossil
fuels, the cleaner crypto mining will become. But even if
bitcoin is powered by renewable energy, “that doesn’t make
it sustainable,” says Kaveh
Madani, director of the United
Nations University Institute for
Water, Environment, and Health.
Mining burns through valuable
resources that could otherwise
be used to meet existing energy
needs, Madani says.
For Goodman, relaxing into
bitcoin-heated water is a completely justifiable use of energy.
09
It soothes the muscles, calms
the mind, and challenges current economic structures, all at
the same time. ■
Carrie Klein is a freelance
journalist based in New York
City.
4/2/25 2:1 PM
10
The Download
BY ARIEL ABERG-RIGER
Changing
channels
AM radio has long served as a critical
communication tool. What happens if it
goes away?
Ariel Aberg-Riger is the author of America Redux:
Visual Stories from Our Dynamic History.
MJ25-front_thedownload.indd 10
4/2/25 2:1 PM
The Download
MJ25-front_thedownload.indd 11
11
4/2/25 2:1 PM
12
The Download
BY YAAKOV ZINBERG
As more
drones begin
flying, a cloudbased traffic
management
system first
developed at
NASA aims
to keep the
airspace safe.
MJ25-front_thedownload.indd 12
On Thanksgiving weekend of 2013, Jeff Bezos, then
Amazon’s CEO, took to 60 Minutes to make a stunning announcement: Amazon was a few years away
from deploying drones that would deliver packages
to homes in less than 30 minutes.
It lent urgency to a problem that Parimal Kopardekar,
director of the NASA Aeronautics Research Institute,
had begun thinking about earlier that year.
“How do you manage and accommodate largescale drone operations without overloading the air
traffic control system?” Kopardekar, who goes by
PK, recalls wondering. Busy managing all airplane
takeoffs and landings, air traffic controllers clearly
wouldn’t have the capacity to oversee the fleets of
package-delivering drones Amazon was promising.
The solution PK devised, which subsequently
grew into a collaboration between federal agencies,
researchers, and industry, is a system called unmannedaircraft-system traffic management, or UTM. Instead
of verbally communicating with air traffic controllers,
drone operators using UTM share their intended flight
paths with each other via a cloud-based network.
This highly scalable approach may finally open the
skies to a host of commercial drone applications that
have yet to materialize. Amazon Prime Air launched
in 2022 but was put on hold after crashes at a testing
facility, for example. On any given day, only 8,500 or so
unmanned aircraft fly in US airspace, the vast majority
of which are used for recreational purposes rather than
for services like search and rescue missions, real estate
inspections, video surveillance, or farmland surveys.
One obstacle to wider use has been concern over
possible midair drone-to-drone collisions. (Drones
are typically restricted to airspace below 400 feet and
their access to airports is limited, which significantly
lowers the risk of drone-airplane collisions.) Under
Federal Aviation Administration regulations, drones
generally cannot fly beyond an operator’s visual line
of sight, limiting flights to about a third of a mile. This
prevents most collisions but also most use cases, such
as delivering medication to a patient’s doorstep or
dispatching a police drone to an active crime scene
so first responders can better prepare before arriving.
Now, though, drone operators are increasingly
incorporating UTM into their flights. The system
uses path planning algorithms, like those that run in
Google Maps, to chart a course that considers not only
weather and obstacles like buildings and trees but the
flight paths of nearby drones. It’ll automatically reroute
a flight before takeoff if another drone has reserved
the same volume of airspace at the same time, making
the new flight trajectory visible to subsequent pilots.
Drones can then fly autonomously to and from their
destination, and no air traffic controller is required.
Over the past decade, NASA and industry have
demonstrated to the FAA through a series of tests
that drones can safely maneuver around each other by
adhering to UTM. And last summer, the agency gave
the go-ahead for multiple drone delivery companies
using UTM to begin flying simultaneously in the same
airspace above Dallas—a first in US aviation history.
Drone operators without in-house UTM capabilities
have also begun licensing UTM services from FAAapproved third-party providers.
UTM only works if all participants abide by the
same rules and agree to share data, and it’s enabled
DOMINIC HART/NASA
Air traffic
control for
drones is
taking off
Drones flying
over Reno,
Nevada, during
one of NASA’s UTM
tests in 2019.
4/2/25 2:1 PM
The Download
13
3 Things
BY CAIWEI CHEN
COURTESY OF CAIWEI CHEN
A new play
about OpenAI
a level of collaboration unusual for companies competing to gain a foothold in a young, hot field, notes
Peter Sachs, head of airspace integration strategy at
Zipline, a drone delivery company based in South
San Francisco that’s approved to use UTM.
“We all agree that we need to collaborate on the
practical, behind-the-scenes nuts and bolts to make
sure that this preflight deconfliction for drones works
really well,” Sachs says. (“Strategic deconfliction” is
the technical term for processes that minimize dronedrone collisions.) Zipline and the drone delivery companies Wing, Flytrex, and DroneUp all operate in the
Dallas area and are racing to expand to more cities,
yet they disclose where they’re flying to one another
in the interest of keeping the airspace conflict-free.
Greater adoption of UTM may be on the way. The
FAA is expected to soon release a new rule called Part
108 that may allow operators to fly beyond visual line
of sight if, among other requirements, they have some
UTM capability, eliminating the need for the difficultto-obtain waiver the agency currently requires for these
flights. To safely manage this additional drone traffic,
drone companies will have to continue working together
to keep their aircraft out of each other’s way.
Yaakov Zinberg is a writer based in Cambridge,
Massachusetts.
MJ25-front_thedownload.indd 13
I recently saw Doomers, a
new play by Matthew Gasda
about the aborted 2023
coup at OpenAI, here represented by a fictional company called MindMesh. The
action is set almost entirely in
a meeting room; the first act
follows executives immediately after the firing of company CEO Seth (a stand-in for
Sam Altman), and the second
re-creates the board negotiations that determined his fate.
It’s a solid attempt to capture
the zeitgeist of Silicon Valley’s
AI frenzy and the world’s moral
panic over artificial intelligence,
but the rapid-fire, high-stakes
exchanges mean it sometimes
seems to get lost in its own
verbosity.
Themed dinner
parties and culinary
experiments
The vastness of Chinese
cuisine defies easy
categorization, and even in
a city with no shortage of
options, I often find myself
cooking—not just to recapture
something closer to home, but
to create a home unlike one
that ever existed. Recently,
I’ve been experimenting
with a Chinese take on the
charcuterie board—pairing
toasted steamed buns, called
mantou, with furu, a fermented
tofu spread that is sharp,
pungent, and full of umami.
Sewing and copying
my own clothes
I started sewing three years
ago, but only in the past year
have I begun making clothes
from scratch. As a lover of vintage fashion—especially ’80s
silhouettes—I started out with
old patterns I found on Etsy.
But recently, I tried something
new: copying a beloved dress I
bought in a thrift store in Beijing
years ago. Doing this is quite
literally a process of reverseengineering—pinning the garment down, tracing its seams,
deconstructing its logic, and
rebuilding it. At times my brain
feels like an old Mac hitting its
GPU limit. But when it works, it
feels like a small act of magic.
It’s an exercise in certainty, the
very thing that drew me to fashion in the first place—a chance
to inhabit something that feels
like an extension of myself. ■
Caiwei Chen is a reporter at
MIT Technology Review
focusing on China. She is
currently based in Brooklyn.
4/2/25 2:1 PM
14
The Download
BY MATTHEW PONSFORD
A project in the Maldives aims to capture
moving sand to protect the archipelago
from erosion and rising seas.
In satellite images, the 20-odd coral atolls of the
Maldives look something like skeletal remains or
chalk lines at a crime scene. But these landforms,
which circle the peaks of a mountain range that has
vanished under the Indian Ocean, are far from inert.
They’re the products of living processes—places
where coral has grown toward the surface over hundreds of thousands of years. Shifting ocean currents
have gradually pushed sand—made from broken-up
bits of this same coral—into more than 1,000 other
islands that poke above the surface.
But these currents can also be remarkably transient, constructing new sandbanks or washing them
away in a matter of weeks. In the coming decades, the
daily lives of the half-million people who live on this
archipelago—the world’s lowest-lying nation—will
depend on finding ways to keep a solid foothold amid
these shifting sands. More than 90% of the islands
have experienced severe erosion, and climate change
could make much of the country uninhabitable by the
middle of the century.
Off one atoll, just south of the Maldives’ capital, Malé, researchers are testing one way to capture sand in strategic locations—to grow islands,
rebuild beaches, and protect coastal communities
from sea-level rise. Swim 10 minutes out into the
En’boodhoofinolhu Lagoon and you’ll find the Ramp
Ring, an unusual structure made up of six toughskinned geotextile bladders. These submerged bags,
part of a recent effort called the Growing Islands
project, form a pair of parentheses separated by
90 meters (around 300 feet).
The bags, each about two meters tall, were deployed
in December 2024, and by February, underwater
images showed that sand had climbed about a meter
and a half up the surface of each one, demonstrating
how passive structures can quickly replenish beaches
and, in time, build a solid foundation for new land.
“There’s just a ton of sand in there. It’s really looking
good,” says Skylar Tibbits, an architect and founder of
MJ25-front_thedownload.indd 14
the MIT Self-Assembly Lab, which is developing the
project in partnership with the Malé-based climate
tech company Invena.
The Self-Assembly Lab designs material technologies that can be programmed to transform or
“self-assemble” in the air or underwater, exploiting
natural forces like gravity, wind, waves, and sunlight.
Its creations include sheets of wood fiber that form
into three-dimensional structures when splashed with
water, which the researchers hope could be used for
tool-free flat-pack furniture.
Growing Islands is their largest-scale undertaking
yet. Since 2017, the project has deployed 10 experiments in the Maldives, testing different materials,
locations, and strategies, including inflatable structures and mesh nets. The Ramp Ring is many times
larger than previous deployments and aims to overcome their biggest limitation.
In the Maldives, the direction of the currents
changes with the seasons. Past experiments have
been able to capture only one seasonal flow, meaning
they lie dormant for months of the year. By contrast,
the Ramp Ring is “omnidirectional,” capturing sand
year-round. “It’s basically a big ring, a big loop, and
no matter which monsoon season and which wave
direction, it accumulates sand in the same area,”
Tibbits says.
The approach points to a more sustainable way to
protect the archipelago, whose growing population
is supported by an economy that caters to 2 million
annual tourists drawn by its white beaches and teeming coral reefs. Most of the country’s 187 inhabited
islands have already had some form of human intervention to reclaim land or defend against erosion,
such as concrete blocks, jetties, and breakwaters.
Since the 1990s, dredging has become by far the most
significant strategy. Boats equipped with high-power
pumping systems vacuum up sand from one part of
the seabed and spray it into a pile somewhere else.
This temporary process allows resort developers and
densely populated islands like Malé to quickly replenish beaches and build limitlessly customizable islands.
But it also leaves behind dead zones where sand has
been extracted—and plumes of sediment that cloud the
water with a sort of choking marine smog. Last year,
INVENA & SELF-ASSEMBLY LAB, MIT
Building islands with ocean
currents
4/3/25
:53 M
The Ramp Ring,
a set of six
submerged bags,
is deployed in
the Maldives
to test using
current-driven
sand to fight
erosion.
MJ25-front_thedownload.indd 15
the government placed a temporary ban on dredging
to prevent damage to reef ecosystems, which were
already struggling amid spiking ocean temperatures.
Holly East, a geographer at the University of
Northumbria, says Growing Islands’ structures offer
an exciting alternative to dredging. But East, who is
not involved in the project, warns that they must be
sited carefully to avoid interrupting sand flows that
already build up islands’ coastlines.
To do this, Tibbits and Invena cofounder Sarah
Dole are conducting long-term satellite analysis of
the En’boodhoofinolhu Lagoon to understand how
sediment flows move around atolls. On the basis of
this work, the team is currently spinning out a predictive coastal intelligence platform called Littoral.
The aim is for it to be “a global health monitoring
system for sediment transport,” Dole says. It’s meant
not only to show where beaches are losing sand but
to “tell us where erosion is going to happen,” allowing
government agencies and developers to know where
new structures like Ramp Rings can best be placed.
Growing Islands has been supported by the National
Geographic Society, MIT, the Sri Lankan engineering
group Sanken, and tourist resort developers. In 2023,
it got a big bump from the US Agency for International
Development: a $250,000 grant that funded the construction of the Ramp Ring deployment and would
have provided opportunities to scale up the approach.
But the termination of nearly all USAID contracts following the inauguration of President Trump means
the project is looking for new partners. ■
Matthew Ponsford is a freelance reporter based in
London.
4/2/25 2:1 PM
16
The Download
Clamor: How Noise Took Over the World and How
We Can Take It Back
By Chris Berdik (W.W. Norton & Company, 2025)
EC Comics Library: Weird Science, Vol. 1
By Grant Geissman (Taschen, 2025)
Inspired by the pulp sci-fi stories of
their youth, publisher Bill Gaines and
artist Al Feldstein drafted the initial
issues of what would become the first
true serialized science fiction magazine,
Weird Science. The first issue appeared
in 1950 with scientific “SuspenStories”
they “dared you to read.” The bimonthly
issues that followed featured a gamut of
what are now classic tropes of the genre: Martian invasions,
murderous androids, glittering utopias, and all-female planets. This nearly 500-page compendium showcases the first
11 issues. What was Gaines’s next act? He was publisher of
Mad magazine for 40 years.
Everything Must Go: The Stories We Tell About
the End of the World
By Dorian Lynskey (Pantheon, 2025)
“The signal fact about the end of the
world is that it has not happened yet,
despite numerous predictions,” writes
Lynskey. Having explored centuries of
preoccupation with “The End,” Lynskey
delves into everything from the anxiety
wrought by the emergence of the atomic
bomb to Ray Bradbury’s stories to the
threat of AI. He says he found his
research not depressing but, rather, something of a relief.
After all, “everybody dies, everything ends—but not yet.
Not yet.”
MJ25-front_thedownload.indd 16
Apocalypse: How Catastrophe Transformed
Our World and Can Forge New Futures
By Lizzie Wade (Harper, 2025)
Droughts, plagues, climate change, and
conquest: Human history is full of
society-ending cataclysms. “But archaeologists see apocalypse differently from
the rest of us,” writes journalist Lizzie
Wade. These events are not just endings,
she says; they are opportunities for survival, change, and renewal. In this surprisingly hopeful book, Wade reports on
what archaeologists see when they look at sites of upheaval
and invites us to look at the past—and our own troubled
time—in a new way.
Everything Is Tuberculosis: The History and
Persistence of Our Deadliest Infection
By John Green (Penguin Random House, 2025)
A new layer of relevance might be
attached to John Green’s book as measles
outbreaks and attacks against vaccines
spread. Best known for his YA novels,
Green became interested in TB after
meeting a young boy with the disease in
Sierra Leone. The resulting book is a
sweeping look at the social, economic,
colonial, and racist forces that still allow
tuberculosis to kill over 1.25 million people per year, even
when a cure exists. His analytical yet accessible account
picks apart how “an illness could quietly shape so much of
human history” and shows that a better world is possible. ■
COURTESY OF THE PUBLISHERS
TR Bookshelf
Noise is an often overlooked menace: It
may be in the background, but the louder
it gets, the worse its impacts. It causes
migraines, exacerbates PTSD for veterans, stresses animals. Noise-canceling
headphones can help, but instead of tuning out the world completely, argues
Berdik, we should be thinking about the
sounds of the shared places we want to
create. He also urges us to begin paying attention to noise
threats of the future, warning of a “looming sonic surge”
brought on by things like air taxis and delivery drones.
Sonic tranquility, he writes, “is fast becoming a luxury
product for those who can afford it.”
4/3/25
:53 M
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Untitled-5 1
12/3/24 2:2 PM
18
Profile
This Icelandic architect wants to build cities
out of lava. By Elissaveta M. Brandon
Above: Arnhildur Pálmadóttir, an
Icelandic architect, has designed
buildings with half the typical
carbon footprint and is hoping to use
lava as a building material.
Opposite: Set in 2150, her
speculative film Lavaforming
presents a fictional city built from
molten lava.
MJ25-front_profile.indd 18
Arnhildur Pálmadóttir was around three years old when she saw
a red sky from her living room window. A volcano was erupting
about 25 miles away from where she lived on the northeastern
coast of Iceland. Though it posed no immediate threat, its ominous presence seeped into her subconscious, populating her
dreams with streaks of light in the night sky.
Fifty years later, these “gloomy, strange dreams,” as Pálmadóttir
now describes them, have led to a career as an architect with
an extraordinary mission: to harness molten lava and build cities out of it.
Pálmadóttir today lives in Reykjavik, where she runs her own
architecture studio, S.AP Arkitektar, and the Icelandic branch of
the Danish architecture company Lendager, which specializes
in reusing building materials.
The architect believes the lava that flows from a single eruption could yield enough building material to lay the foundations
of an entire city. She has been researching this possibility for
more than five years as part of a project she calls Lavaforming.
Together with her son and colleague Arnar Skarphéðinsson, she
has identified three potential techniques: drill straight into magma
pockets and extract the lava; channel molten lava into pre-dug
COURTESY OF S.AP ARKITEKTAR
The
new hotness
3/2 /25 5:52 PM
Profile
trenches that could form a city’s foundations; or 3D-print bricks
from molten lava in a technique similar to the way objects can
be printed out of molten glass.
Pálmadóttir and Skarphéðinsson first presented the concept
during a talk at Reykjavik’s DesignMarch festival in 2022. This year
they are producing a speculative film set in 2150, in an imaginary
city called Eldborg. Their film, titled Lavaforming, follows the
lives of Eldborg’s residents and looks back on how they learned
to use molten lava as a building material. It will be presented
at the Venice Biennale, a leading architecture festival, in May.
Buildings and construction materials like concrete and steel
currently contribute a staggering 37% of the world’s annual carbon
dioxide emissions. Many architects are advocating for the use
of natural or preexisting materials, but mixing earth and water
into a mold is one thing; tinkering with 2,000 °F lava is another.
Still, Pálmadóttir is piggybacking on research already being
done in Iceland, which has 30 active volcanoes. Since 2021,
eruptions have intensified in the Reykjanes Peninsula, which is
close to the capital and to tourist hot spots like the Blue Lagoon.
In 2024 alone, there were six volcanic eruptions in that area.
This frequency has given volcanologists opportunities to study
MJ25-front_profile.indd 19
19
how lava behaves after a volcano erupts. “We try to follow this
beast,” says Gro Birkefeldt M. Pedersen, a volcanologist at the
Icelandic Meteorological Office (IMO), who has consulted with
Pálmadóttir on a few occasions. “There is so much going on, and
we’re just trying to catch up and be prepared.”
Pálmadóttir’s concept assumes that many years from now,
volcanologists will be able to forecast lava flow accurately enough
for cities to plan on using it in building. They will know when
and where to dig trenches so that when a volcano erupts, the lava
will flow into them and solidify into either walls or foundations.
Today, forecasting lava flows is a complex science that requires
remote sensing technology and tremendous amounts of computational power to run simulations on supercomputers. The
IMO typically runs two simulations for every new eruption—one
based on data from previous eruptions, and another based on
additional data acquired shortly after the eruption (from various
sources like specially outfitted planes). With every event, the
team accumulates more data, which makes the simulations of
lava flow more accurate. Pedersen says there is much research
yet to be done, but she expects “a lot of advancement” in the
next 10 years or so.
3/2 /25 5:52 PM
Profile
To design the speculative city of Eldborg for their film,
Pálmadóttir and Skarphéðinsson used 3D-modeling software
similar to what Pedersen uses for her simulations. The city is
primarily built on a network of trenches that were filled with
lava over the course of several eruptions, while buildings are
constructed out of lava bricks. “We’re going to let nature design
the buildings that will pop up,” says Pálmadóttir.
The aesthetic of the city they envision will be less modernist and more fantastical—a bit “like [Gaudi’s] Sagrada Familia,”
says Pálmadóttir. But the aesthetic output is not really the point;
the architects’ goal is to galvanize architects today and spark an
urgent discussion about the impact of climate change on our
cities. She stresses the value of what can only be described as
moonshot thinking. “I think it is important for architects not
to be only in the present,” she told me. “Because if we are only
in the present, working inside the system, we won’t change
anything.”
Pálmadóttir was born in 1972 in Húsavik, a town known as
the whale-watching capital of Iceland. But she was more interested in space and technology and spent a lot of time flying with
her father, a construction engineer who owned a small plane.
She credits his job for the curiosity she developed about science and “how things were put together”—an inclination that
proved useful later, when she started researching volcanoes. So
was the fact that Icelanders “learn to live with volcanoes from
birth.” At 21, she moved to Norway, where she spent seven years
working in 3D visualization before returning to Reykjavik and
enrolling in an architecture program at the Iceland University
of the Arts. But things didn’t click until she moved to Barcelona
for a master’s degree at the Institute for Advanced Architecture
of Catalonia. “I remember being there and feeling, finally, like I
was in the exact right place,” she says.
Before, architecture had seemed like a commodity and architects like “slaves to investment companies,” she says. Now, it felt
like a path with potential.
She returned to Reykjavik in 2009 and worked as an architect until she founded S.AP (for “studio Arnhildur Pálmadóttir”)
Arkitektar in 2018; her son started working with her in 2019 and
officially joined her as an architect this year, after graduating from
the Southern California Institute of Architecture.
In 2021, the pair witnessed their first eruption up close, near
the Fagradalsfjall volcano on the Reykjanes Peninsula. It was there
that Pálmadóttir became aware of the sheer quantity of material
coursing through the planet’s veins, and the potential to divert it
into channels.
Lava has already proved to be a strong, long-lasting building
material—at least in its solid state. When it cools, it solidifies into
volcanic rock like basalt or rhyolite. The type of rock depends
on the composition of the lava, but basaltic lava—like the kind
found in Iceland and Hawaii—forms one of the hardest rocks on
Earth, which means that structures built from this type of lava
would be durable and resilient.
MJ25-front_profile.indd 20
For years, architects in Mexico, Iceland, and Hawaii (where
lava is widely available) have built structures out of volcanic
rock. But quarrying that rock is an energy-intensive process
that requires heavy machines to extract, cut, and haul it, often
across long distances, leaving a big carbon footprint. Harnessing
lava in its molten state, however, could unlock new methods for
sustainable construction. Jeffrey Karson, a professor emeritus
at Syracuse University who specializes in volcanic activity and
COURTESY OF S.AP ARKITEKTAR
20
3/2 /25 5:52 PM
Lava has proved to be a strong,
durable building material, at least
in its solid state. To explore
its potential,Pálmadóttir and
Skarphéðinsson envision a city built
on a network of trenches that have
filled with lava over the course of
several eruptions, while buildings
are constructed with lava bricks.
Profile
21
who cofounded the Syracuse University Lava Project, agrees
that lava is abundant enough to warrant interest as a building
material. To understand how it behaves, Karson has spent the
past 15 years performing over a thousand controlled lava pours
from giant furnaces. If we figure out how to build up its strength
as it cools, he says, “that stuff has a lot of potential.”
In his research, Karson found that inserting metal rods into
the lava flow helps reduce the kind of uneven cooling that would
lead to thermal cracking—and therefore makes the material
stronger (a bit like rebar in concrete). Like glass and other molten materials, lava behaves differently depending on how fast
it cools. When glass or lava cools slowly, crystals start forming,
strengthening the material. Replicating this process—perhaps
in a kiln—could slow down the rate of cooling and let the lava
become stronger. This kind of controlled cooling is “easy to do
on small things like bricks,” says Karson, so “it’s not impossible
to make a wall.”
Pálmadóttir is clear-eyed about the challenges before her.
She knows the techniques she and Skarphéðinsson are exploring may not lead to anything tangible in their lifetimes, but they
still believe that the ripple effect the projects could create in the
architecture community is worth pursuing.
Both Karson and Pedersen caution that more experiments are
necessary to study this material’s potential. For Skarphéðinsson,
that potential transcends the building industry. More than 12
years ago, Icelanders voted that the island’s natural resources,
like its volcanoes and fishing waters, should be declared national
property. That means any city built from lava flowing out of these
volcanoes would be controlled not by deep-pocketed individuals or companies, but by the nation itself. (The referendum was
considered illegal almost as soon as it was approved by voters
and has since stalled.)
For Skarphéðinsson, the Lavaforming project is less about the
material than about the “political implications that get brought to
the surface with this material.” “That is the change I want to see
in the world,” he says. “It could force us to make radical changes
and be a catalyst for something”—perhaps a social megalopolis
where citizens have more say in how resources are used and
profits are shared more evenly.
Cynics might dismiss the idea of harnessing lava as pure folly.
But the more I spoke with Pálmadóttir, the more convinced I
became. It wouldn’t be the first time in modern history that a
seemingly dangerous idea (for example, drilling into scalding
pockets of underground hot springs) proved revolutionary.
Once entirely dependent on oil, Iceland today obtains 85% of
its electricity and heat from renewable sources. “[My friends]
probably think I’m pretty crazy, but they think maybe we could
be clever geniuses,” she told me with a laugh. Maybe she is a
little bit of both.
Elissaveta M. Brandon is a regular contributor to Fast
Company and Wired.
MJ25-front_profile.indd 21
3/2 /25 5:52 PM
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Andrew Kudless, founder of the
design studio Matsys, created this
series with Stable Diffusion and the
prompt “machiya” (a reference to
traditional merchant townhouses
found throughout Japan). “As the
resolution increases, the model can’t
seem to focus on the specifics of
the traditional machiya typology,” he
explains, “so instead of machiya, we
get density, nature, and commerce.
Essentially a stereotype of Japanese
urbanism.”
For more work using AI to push the
bounds of architecture, see page 50.
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24
Forget one-click creativity.
These artists and musicians aren’t ceding their work
to AI. Instead, they’re finding new ways to use it,
injecting more friction, challenge, and serendipity into
the creative process.
By
Will Douglas Heaven
How
AI
can help
supercharge
creativity
MJ25-feature_art.indd 24
Sometimes Lizzie Wilson shows up to a rave with her
AI sidekick.
One weeknight this past February, Wilson plugged
her laptop into a projector that threw her screen onto
the wall of a low-ceilinged loft space in East London.
A small crowd shuffled in the glow of dim pink lights.
Wilson sat down and started programming.
Techno clicks and whirs thumped from the venue’s
speakers. The audience watched, heads nodding, as
Wilson tapped out code line by line on the projected
screen—tweaking sounds, looping beats, pulling a face
when she messed up.
Wilson is a live coder. Instead of using purpose-built
software like most electronic music producers, live
coders create music by writing the code to generate it
on the fly. It’s an improvised performance art known
as algorave.
“It’s kind of boring when you go to watch a show
and someone’s just sitting there on their laptop,” she
says. “You can enjoy the music, but there’s a performative aspect that’s missing. With live coding, everyone can see what it is that I’m typing. And when I’ve
had my laptop crash, people really like that. They
start cheering.”
Taking risks is part of the vibe. And so Wilson likes
to dial up her performances one more notch by riffing off what she calls a live-coding agent, a generative
AI model that comes up with its own beats and loops
to add to the mix. Often the model suggests sound
combinations that Wilson hadn’t thought of. “You get
these elements of surprise,” she says. “You just have
to go for it.”
Wilson, a researcher at the Creative Computing
Institute at the University of the Arts London, is just
one of many working on what’s known as co-creativity
or more-than-human creativity. The idea is that AI
can be used to inspire or critique creative projects,
helping people make things that they would not have
made by themselves. She and her colleagues built the
live-coding agent to explore how artificial intelligence
can be used to support human artistic endeavors—in
Wilson’s case, musical improvisation.
It’s a vision that goes beyond the promise of existing generative tools put out by companies like OpenAI
and Google DeepMind. Those can automate a striking
range of creative tasks and offer near-instant gratification—but at what cost? Some artists and researchers
fear that such technology could turn us into passive
consumers of yet more AI slop.
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25
Researcher Lizzie
Wilson performs at an
algorave in London.
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26
And so they are looking for ways to inject human
creativity back into the process. The aim is to develop
AI tools that augment our creativity rather than strip
it from us—pushing us to be better at composing
music, developing games, designing toys, and much
more—and lay the groundwork for a future in which
humans and machines create things together.
Ultimately, generative models could offer artists
and designers a whole new medium, pushing them to
make things that couldn’t have been made before, and
give everyone creative superpowers.
Explosion of creativity
There’s no one way to be creative, but we all do it. We
make everything from memes to masterpieces, infant
doodles to industrial designs. There’s a mistaken belief,
typically among adults, that creativity is something you
grow out of. But being creative—whether cooking, singing
in the shower, or putting together super-weird TikToks—
is still something that most of us do just for the fun of it.
It doesn’t have to be high art or a world-changing idea
(and yet it can be). Creativity is basic human behavior;
it should be celebrated and encouraged.
When generative text-to-image models like
Midjourney, OpenAI’s DALL-E, and the popular opensource Stable Diffusion arrived, they sparked an explosion of what looked a lot like creativity. Millions of
people were now able to create remarkable images of
pretty much anything, in any style, with the click of a
button. Text-to-video models came next. Now startups
like Udio (see p. 36), are developing similar tools for
music. Never before have the fruits of creation been
within reach of so many.
But for a number of researchers and artists, the hype
around these tools has warped the idea of what creativity really is. “If I ask the AI to create something for
me, that’s not me being creative,” says Jeba Rezwana,
who works on co-creativity at Towson University in
Maryland. “It’s a one-shot interaction: You click on it
and it generates something and that’s it. You cannot
say ‘I like this part, but maybe change something here.’
You cannot have a back-and-forth dialogue.”
Rezwana is referring to the way most generative
models are set up. You can give the tools feedback
and ask them to have another go. But each new result
is generated from scratch, which can make it hard to
nail exactly what you want. As the filmmaker Walter
Woodman put it last year after his art collective Shy
Kids made a short film with OpenAI’s text-to-video
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PREVIOUS SPREAD: JONATHAN REUS; DEVOLVER DIGITAL (OPPOSITE TOP); COURTESY OF MIKE COOK (PORTRAIT, OPPOSITE BOTTOM)
27
model for the first time: “Sora is a slot machine as to
what you get back.”
What’s more, the latest versions of some of these
generative tools do not even use your submitted prompt
as is to produce an image or video (at least not on their
default settings). Before a prompt is sent to the model,
the software edits it—often by adding dozens of hidden words—to make it more likely that the generated
image will appear polished.
“Extra things get added to juice the output,” says
Mike Cook, a computational creativity researcher at
King’s College London. “Try asking Midjourney to
give you a bad drawing of something—it can’t do it.”
These tools do not give you what you want; they give
you what their designers think you want.
All of which is fine if you just need
a quick image and don’t care too much
about the details, says Nick BryanKinns, also at the Creative Computing
Institute: “Maybe you want to make
a Christmas card for your family or a
flyer for your community cake sale.
These tools are great for that.”
In short, existing generative models have made it easy to create, but
they have not made it easy to be creative. And there’s a big difference
between the two. For Cook, relying
on such tools could in fact harm people’s creative development in the
long run. “Although many of these
creative AI systems are promoted
as making creativity more accessible,” he wrote in
a paper published last year, they might instead have
“adverse effects on their users in terms of restricting
their ability to innovate, ideate, and create.” Given
how much generative models have been championed
for putting creative abilities at everyone’s fingertips,
the suggestion that they might in fact do the opposite is damning.
He’s far from the only researcher worrying about
the cognitive impact of these technologies. In February
a team at Microsoft Research Cambridge published a
report concluding that generative AI tools “can inhibit
critical engagement with work and can potentially lead
to long-term overreliance on the tool and diminished
skill for independent problem-solving.” The researchers found that with the use of generative tools, people’s
effort “shifts from task execution to task stewardship.”
MJ25-feature_art.indd 27
Cook is concerned that generative tools don’t let
you fail—a crucial part of learning new skills. We have
a habit of saying that artists are gifted, says Cook. But
the truth is that artists work at their art, developing
skills over months and years.
“If you actually talk to artists, they say, ‘Well, I got
good by doing it over and over and over,’” he says. “But
failure sucks. And we’re always looking at ways to get
around that.”
Generative models let us skip the frustration of
doing a bad job.
“Unfortunately, we’re removing the one thing that
you have to do to develop creative skills for yourself,
which is fail,” says Cook. “But absolutely nobody wants
to hear that.”
Surprise me
Mike Cook, a
computational
creativity
researcher,
used an AI
tool to design
a new level
for the game
Disc Room
(opposite,
above), in
which players
navigate a
room of moving
buzz saws.
The result
was a room
where none
of the discs
actually
moved
(below).
And yet it’s not all bad news. Artists and
researchers are buzzing at the ways generative tools could empower creators,
pointing them in surprising new directions and steering them away from dead
ends. Cook thinks the real promise of
AI will be to help us get better at what
we want to do rather than doing it for
us. For that, he says, we’ll need to create
new tools, different from the ones we
have now. “Using Midjourney does not
do anything for me—it doesn’t change
anything about me,” he says. “And I
think that’s a wasted opportunity.”
Ask a range of researchers studying
creativity to name a key part of the creative process and
many will say: reflection. It’s hard to define exactly,
but reflection is a particular type of focused, deliberate thinking. It’s what happens when a new idea hits
you. Or when an assumption you had turns out to be
wrong and you need to rethink your approach. It’s the
opposite of a one-shot interaction.
Looking for ways that AI might support or encourage reflection—asking it to throw new ideas into the
mix or challenge ideas you already hold—is a common
thread across co-creativity research. If generative tools
like DALL-E make creation frictionless, the aim here is
to add friction back in. “How can we make art without
friction?” asks Elisa Giaccardi, who studies design at
the Polytechnic University of Milan in Italy. “How can
we engage in a truly creative process without material
that pushes back?”
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Take Wilson’s live-coding agent. She claims that
it pushes her musical improvisation in directions she
might not have taken by herself. Trained on public
code shared by the wider live-coding community,
the model suggests snippets of code that are closer
to other people’s styles than her own. This makes it
more likely to produce something unexpected. “Not
because you couldn’t produce it yourself,” she says.
“But the way the human brain works, you tend to fall
back on repeated ideas.”
Last year, Wilson took part in a study run by BryanKinns and his colleagues in which they surveyed six
experienced musicians as they used a variety of generative models to help them compose a piece of music.
The researchers wanted to get a sense of what kinds of
interactions with the technology were
useful and which were not.
The participants all said they liked
it when the models made surprising
suggestions, even when those were
the result of glitches or mistakes.
Sometimes the results were simply
better. Sometimes the process felt fresh
and exciting. But a few people struggled
with giving up control. It was hard to
direct the models to produce specific
results or to repeat results that the
musicians had liked. “In some ways
it’s the same as being in a band,” says
Bryan-Kinns. “You need to have that
sense of risk and a sense of surprise,
but you don’t want it totally random.”
Alternative designs
Cook comes at surprise from a different angle: He
coaxes unexpected insights out of AI tools that he has
developed to co-create video games. One of his tools,
Puck, which was first released in 2022, generates
designs for simple shape-matching puzzle games like
Candy Crush or Bejeweled. A lot of Puck’s designs are
experimental and clunky—don’t expect it to come up
with anything you are ever likely to play. But that’s not
the point: Cook uses Puck—and a newer tool called
Pixie—to explore what kinds of interactions people
might want to have with a co-creative tool.
Pixie can read computer code for a game and tweak
certain lines to come up with alternative designs. Not
long ago, Cook was working on a copy of a popular
game called Disc Room, in which players have to cross
MJ25-feature_art.indd 28
Researcher
Anne
Arzberger
developed
experimental
AI tools
to come up
with genderneutral
toy designs
(opposite).
a room full of moving buzz saws. He asked Pixie to help
him come up with a design for a level that skilled and
unskilled players would find equally hard. Pixie designed
a room where none of the discs actually moved. Cook
laughs: It’s not what he expected. “It basically turned
the room into a minefield,” he says. “But I thought it
was really interesting. I hadn’t thought of that before.”
Pushing back on assumptions, or being challenged,
is part of the creative process, says Anne Arzberger, a
researcher at the Delft University of Technology in the
Netherlands. “If I think of the people I’ve collaborated
with best, they’re not the ones who just said ‘Yes, great’
to every idea I brought forth,” she says. “They were
really critical and had opposing ideas.”
She wants to build tech that provides a similar sounding board. As part of a project called
Creating Monsters, Arzberger developed two experimental AI tools that
help designers find hidden biases in
their designs. “I was interested in ways
in which I could use this technology to
access information that would otherwise be difficult to access,” she says.
For the project, she and her colleagues looked at the problem of
designing toy figures that would be
gender neutral. She and her colleagues
(including Giaccardi) used Teachable
Machine, a web app built by Google
researchers in 2017 that makes it easy
to train your own machine-learning
model to classify different inputs, such
as images. They trained this model with a few dozen
images that Arzberger had labeled as being masculine,
feminine, or gender neutral.
Arzberger then asked the model to identify the genders of new candidate toy designs. She found that quite
a few designs were judged to be feminine even when
she had tried to make them gender neutral. She felt that
her views of the world—her own hidden biases—were
being exposed. But the tool was often right: It challenged her assumptions and helped the team improve
the designs. The same approach could be used to assess
all sorts of design characteristics, she says.
Arzberger then used a second model, a version of
a tool made by the generative image and video startup
Runway, to come up with gender-neutral toy designs of
its own. First the researchers trained the model to generate and classify designs for male- and female-looking
COURTESY OF ANNE ARZBERGER
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toys. They could then ask the tool to find a design that
was exactly midway between the male and female
designs it had learned.
Generative models can give feedback on designs
that human designers might miss by themselves, she
says: “We can really learn something.”
Taking control
The history of technology is full of breakthroughs that
changed the way art gets made, from recipes for vibrant
new paint colors to photography to synthesizers. In the
1960s, the Stanford researcher John Chowning spent
years working on an esoteric algorithm that could
manipulate the frequencies of computer-generated
sounds. Stanford licensed the tech to Yamaha, which
built it into its synthesizers—including
the DX7, the cool new sound behind
1980s hits such as Tina Turner’s “The
Best,” A-ha’s “Take On Me,” and Prince’s
“When Doves Cry.”
Bryan-Kinns is fascinated by how
artists and designers find ways to use
new technologies. “If you talk to artists, most of them don’t actually talk
about these AI generative models as a
tool—they talk about them as a material, like an artistic material, like a paint
or something,” he says. “It’s a different
way of thinking about what the AI is
doing.” He highlights the way some
people are pushing the technology to
do weird things it wasn’t designed to
do. Artists often appropriate or misuse these kinds of
tools, he says.
Bryan-Kinns points to the work of Terence Broad,
another colleague of his at the Creative Computing
Institute, as a favorite example. Broad employs techniques like network bending, which involves inserting
new layers into a neural network to produce glitchy
visual effects in generated images, and generating
images with a model trained on no data, which produces
almost Rothko-like abstract swabs of color.
But Broad is an extreme case. Bryan-Kinns sums it
up like this: “The problem is that you’ve got this gulf
between the very commercial generative tools that
produce super-high-quality outputs but you’ve got very
little control over what they do—and then you’ve got
this other end where you’ve got total control over what
they’re doing but the barriers to use are high because
MJ25-feature_art.indd 30
Researcher
Terence Broad
creates
dynamic
images using
a model
trained on no
data, which
produces
almost
Rothko-like
abstract
color fields
(opposite).
you need to be somebody who’s comfortable getting
under the hood of your computer.”
“That’s a small number of people,” he says. “It’s a
very small number of artists.”
Arzberger admits that working with her models was
not straightforward. Running them took several hours,
and she’s not sure the Runway tool she used is even
available anymore. Bryan-Kinns, Arzberger, Cook, and
others want to take the kinds of creative interactions
they are discovering and build them into tools that can
be used by people who aren’t hardcore coders.
Finding the right balance between surprise and
control will be hard, though. Midjourney can surprise,
but it gives few levers for controlling what it produces
beyond your prompt. Some have claimed that writing
prompts is itself a creative act. “But
no one struggles with a paintbrush
the way they struggle with a prompt,”
says Cook.
Faced with that struggle, Cook
sometimes watches his students just
go with the first results a generative
tool gives them. “I’m really interested
in this idea that we are priming ourselves to accept that whatever comes
out of a model is what you asked for,”
he says. He is designing an experiment
that will vary single words and phrases
in similar prompts to test how much
of a mismatch people see between
what they expect and what they get.
But it’s early days yet. In the meantime, companies developing generative models typically
emphasize results over process. “There’s this impressive
algorithmic progress, but a lot of the time interaction
design is overlooked,” says Rezwana.
For Wilson, the crucial choice in any co-creative
relationship is what you do with what you’re given.
“You’re having this relationship with the computer
that you’re trying to mediate,” she says. “Sometimes it
goes wrong, and that’s just part of the creative process.”
When AI gives you lemons—make art. “Wouldn’t it
be fun to have something that was completely antagonistic in a performance—like, something that is
actively going against you—and you kind of have an
argument?” she says. “That would be interesting to
watch, at least.”
Will Douglas Heaven is senior editor for AI at
MIT Technology Review.
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31
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33
Creative
differences
In The Cult of Creativity,
Samuel Franklin excavates the
surprisingly recent history of
creativity—as an idea, an ideal,
and an ideology.
BY
ILLUSTRATION
MJ25-feature_Q&A.indd 33
Bryan Gardiner
Tom Humberstone
Americans don’t agree on much these days.
Yet even at a time when consensus reality
seems to be on the verge of collapse, there
remains at least one quintessentially modern
value we can all still get behind: creativity.
We teach it, measure it, envy it, cultivate
it, and endlessly worry about its death. And
why wouldn’t we? Most of us are taught
from a young age that creativity is the key to
everything from finding personal fulfillment
to achieving career success to solving the
world’s thorniest problems. Over the years,
we’ve built creative industries, creative
spaces, and creative cities and populated
them with an entire class of people known
simply as “creatives.” We read thousands of
books and articles each year that teach us
how to unleash, unlock, foster, boost, and
hack our own personal creativity. Then we
read even more to learn how to manage and
protect this precious resource.
Given how much we obsess over it, the
concept of creativity can feel like something
that has always existed, a thing philosophers
and artists have pondered and debated
throughout the ages. While it’s a reasonable assumption, it’s one that turns out to
be very wrong. As Samuel Franklin explains
in his recent book, The Cult of Creativity,
the first known written use of creativity
didn’t actually occur until 1875, “making it
an infant as far as words go.” What’s more,
he writes, before about 1950, “there were
approximately zero articles, books, essays,
treatises, odes, classes, encyclopedia entries,
or anything of the sort dealing explicitly
with the subject of ‘creativity.’”
This raises some obvious questions.
How exactly did we go from never talking
about creativity to always talking about it?
What, if anything, distinguishes creativity from other, older words, like ingenuity, cleverness, imagination, and artistry?
Maybe most important: How did everyone
from kindergarten teachers to mayors,
CEOs, designers, engineers, activists,
and starving artists come to believe that
creativity isn’t just good—personally,
socially, economically—but the answer
to all life’s problems?
Thankfully, Franklin offers some potential answers in his book. A historian and
design researcher at the Delft University of
Technology in the Netherlands, he argues
that the concept of creativity as we now
know it emerged during the post–World
War II era in America as a kind of cultural
salve—a way to ease the tensions and anxieties caused by increasing conformity,
bureaucracy, and suburbanization.
“Typically defined as a kind of trait or
process vaguely associated with artists and
geniuses but theoretically possessed by anyone and applicable to any field, [creativity]
provided a way to unleash individualism
within order,” he writes, “and revive the
spirit of the lone inventor within the maze
of the modern corporation.”
I spoke to Franklin about why we continue to be so fascinated by creativity,
how Silicon Valley became the supposed
epicenter of it, and what role, if any, technologies like AI might have in reshaping
our relationship with it.
3/2 /25 5:55 PM
34
Below: Brainstorming, a new method for
encouraging creative thinking, swept corporate
America in the 1950s. A response to pressure
for new products and new ways of marketing
them, as well as a panic over conformity, it
inspired passionate debate about whether true
creativity should be an individual affair or could
be systematized for corporate use.
Opposite: Staff members at the University of
California’s Institute of Personality Assessment
and Research simulate a situational procedure
involving group interaction, called the Bingo Test.
Researchers of the 1950s hoped to learn how
factors in people’s lives and environments shaped
their creative aptitude.
I’m curious what your personal relationship
to creativity was growing up. What made
you want to write a book about it?
When did you start thinking about creativity
as a kind of cult—one that we’re all a part of?
already eminent in fields that were deemed
creative—writers like Truman Capote and
Norman Mailer, architects like Louis Kahn
and Eero Saarinen—and just give them a
battery of cognitive and psychoanalytic
tests and then write up the results. This was
mostly done by an outfit called the Institute
of Personality Assessment and Research
(IPAR) at Berkeley. Frank Barron and Don
MacKinnon were the two biggest researchers in that group.
Another way psychologists went about
it was to say, all right, that’s not going to be
Like a lot of kids, I grew up thinking that
creativity was this inherently good thing.
For me—and I imagine for a lot of other
people who, like me, weren’t particularly
athletic or good at math and science—
being creative meant you at least had some
future in this world, even if it wasn’t clear
what that future would entail. By the time
I got into college and beyond, the conventional wisdom among the TED Talk register of thinkers—people like Daniel Pink
and Richard Florida—was that creativity
was actually the most important trait to
have for the future. Basically, the creative
people were going to inherit the Earth,
and society desperately needed them if
we were going to solve all of these compounding problems in the world.
On the one hand, as someone who
liked to think of himself as creative, it
was hard not to be flattered by this. On
the other hand, it all seemed overhyped to
me. What was being sold as the triumph
of the creative class wasn’t actually resulting in a more inclusive or creative world
order. What’s more, some of the values
embedded in what I call the cult of creativity seemed increasingly problematic—
specifically, the focus on self-realization,
doing what you love, and following your
passion. Don’t get me wrong—it’s a beautiful vision, and I saw it work out for some
people. But I also started to feel like it was
just a cover for what was, economically
speaking, a pretty bad turn of events for
many people.
Nowadays, it’s quite common to bash
the “follow your passion,” “hustle culture”
idea. But back when I started this project,
the whole move-fast-and-break-things,
disrupter, innovation-economy stuff was
very much unquestioned. In a way, the
idea for the book came from recognizing
that creativity was playing this really interesting role in connecting two worlds: this
world of innovation and entrepreneurship
and this more soulful, bohemian side of
our culture. I wanted to better understand
the history of that relationship.
MJ25-feature_Q&A.indd 34
Similar to something like the “cult of domesticity,” it was a way of describing a historical
moment in which an idea or value system
achieves a kind of broad, uncritical acceptance. I was finding that everyone was selling stuff based on the idea that it boosted
your creativity, whether it was a new office
layout, a new kind of urban design, or the
“Try these five simple tricks” type of thing.
You start to realize that nobody is bothering to ask, “Hey, uh, why do we all need to
be creative again? What even is this thing,
creativity?” It had become this unimpeachable value that no one, regardless of what
side of the political spectrum they fell on,
would even think to question. That, to me,
was really unusual, and I think it signaled
that something interesting was happening.
Your book highlights midcentury efforts by
psychologists to turn creativity into a quantifiable mental trait and the “creative person”
into an identifiable type. How did that play out?
The short answer is: not very well. To study
anything, you of course need to agree on
what it is you’re looking at. Ultimately, I think
these groups of psychologists were frustrated
in their attempts to come up with scientific
criteria that defined a creative person. One
technique was to go find people who were
practical for coming up with a good scientific
standard. We need numbers, and lots and
lots of people to certify these creative criteria. This group of psychologists theorized
that something called “divergent thinking”
was a major component of creative accomplishment. You’ve heard of the brick test,
where you’re asked to come up with many
creative uses for a brick in a given amount
of time? They basically gave a version of
that test to Army officers, schoolchildren,
rank-and-file engineers at General Electric,
all kinds of people. It’s tests like those that
ultimately became stand-ins for what it
means to be “creative.”
Are they still used?
When you see a headline about AI making people more creative, or actually being
3/2 /25 5:55 PM
35
engineer working for a large R&D lab of a
brick-and-mortar manufacturing corporation
and instead raise up the idea of a rebellious
counterculture type tinkering in a garage
making weightless products and experiences. That, I think, has saved it from a lot
of public scrutiny.
Up until recently, we’ve tended to think of
creativity as a human trait, maybe with a few
exceptions from the rest of the animal world.
Is AI changing that?
IMAGES COURTESY INSTITUTE OF PERSONALITY AND SOCIAL RESEARCH, UNIVERSITY OF CALIFORNIA, BERKELEY/THE MONACELLI PRESS
The question of “Can machines be ‘truly
creative’?” is not that interesting, but the
questions of “Can they be wise, honest,
caring?” are more important.
more creative than humans, the tests they
are basing that assertion on are almost
always some version of a divergent thinking
test. It’s highly problematic for a number
of reasons. Chief among them is the fact
that these tests have never been shown to
have predictive value—that’s to say, a third
grader, a 21-year-old, or a 35-year-old who
does really well on divergent thinking tests
doesn’t seem to have any greater likelihood
of being successful in creative pursuits. The
whole point of developing these tests in the
first place was to both identify and predict
creative people. None of them have been
shown to do that.
Reading your book, I was struck by how vague
and, at times, contradictory the concept of
“creativity” was from the beginning. You characterize that as “a feature, not a bug.” How so?
Ask any creativity expert today what they
mean by “creativity,” and they’ll tell you it’s
the ability to generate something new and
useful. That something could be an idea,
a product, an academic paper—whatever.
But the focus on novelty has remained an
aspect of creativity from the beginning. It’s
also what distinguishes it from other similar
words, like imagination or cleverness. But
you’re right: Creativity is a flexible enough
MJ25-feature_Q&A.indd 35
concept to be used in all sorts of ways and
to mean all sorts of things, many of them
contradictory. I think I write in the book
that the term may not be precise, but that
it’s vague in precise and meaningful ways. It
can be both playful and practical, artsy and
technological, exceptional and pedestrian.
That was and remains a big part of its appeal.
Is that emphasis on novelty and utility a part
of why Silicon Valley likes to think of itself as
the new nexus for creativity?
Absolutely. The two criteria go together. In
techno-solutionist, hypercapitalist milieus
like Silicon Valley, novelty isn’t any good if
it’s not useful (or at least marketable), and
utility isn’t any good (or marketable) unless
it’s also novel. That’s why they’re often
dismissive of boring-but-important things
like craft, infrastructure, maintenance, and
incremental improvement, and why they
support art—which is traditionally defined
by its resistance to utility—only insofar
as it’s useful as inspiration for practical
technologies.
At the same time, Silicon Valley loves
to wrap itself in “creativity” because of all
the artsy and individualist connotations. It
has very self-consciously tried to distance
itself from the image of the buttoned-down
When people started defining creativity in
the ’50s, the threat of computers automating
white-collar work was already underway.
They were basically saying, okay, rational
and analytical thinking is no longer ours
alone. What can we do that the computers
can never do? And the assumption was that
humans alone could be “truly creative.” For
a long time, computers didn’t do much to
really press the issue on what that actually
meant. Now they’re pressing the issue. Can
they do art and poetry? Yes. Can they generate novel products that also make sense
or work? Sure.
I think that’s by design. The kinds of
LLMs that Silicon Valley companies have
put forward are meant to appear “creative”
in those conventional senses. Now, whether
or not their products are meaningful or
wise in a deeper sense, that’s another
question. If we’re talking about art, I happen to think embodiment is an important
element. Nerve endings, hormones, social
instincts, morality, intellectual honesty—
those are not things essential to “creativity” necessarily, but they are essential to
putting things out into the world that are
good, and maybe even beautiful in a certain antiquated sense. That’s why I think
the question of “Can machines be ‘truly
creative’?” is not that interesting, but the
questions of “Can they be wise, honest,
caring?” are more important if we’re going
to be welcoming them into our lives as
advisors and assistants.
This interview is based on two conversations
and has been edited and condensed for clarity.
Bryan Gardiner is a writer based in
Oakland, California.
3/2 /25 5:55 PM
36
Replication and creation
Artificial intelligence was barely a term in 1956, when top scientists from the field of computing arrived at Dartmouth College
for a summer conference. The computer scientist John McCarthy
had coined the phrase in the funding proposal for the event, a
gathering to work through how to build machines that could
use language, solve problems like humans, and improve themselves. But it was a good choice, one that captured the organizers’ founding premise: Any feature of human intelligence could
“in principle be so precisely described that a machine can be
made to simulate it.”
In their proposal, the group had listed several “aspects of the
artificial intelligence problem.” The last item on their list, and
in hindsight perhaps the most difficult, was building a machine
that could exhibit creativity and originality.
At the time, psychologists were grappling with how to define
and measure creativity in humans. The prevailing theory—that
creativity was a product of intelligence and high IQ—was fading, but psychologists weren’t sure what to replace it with. The
Dartmouth organizers had one of their own. “The difference
between creative thinking and unimaginative competent thinking
lies in the injection of some randomness,” they wrote, adding that
such randomness “must be guided by intuition to be efficient.”
Nearly 70 years later, following a number of boom-and-bust
cycles in the field, we now have AI models that more or less
follow that recipe. While large language models that generate
text have exploded in the last three years, a different type of AI,
based on what are called diffusion models, is having an unprecedented impact on creative domains. By transforming random
noise into coherent patterns, diffusion models can generate
new images, videos, or speech, guided by text prompts or other
input data. The best ones can create outputs indistinguishable
from the work of people, as well as bizarre, surreal results that
feel distinctly nonhuman.
Now these models are marching into a creative field that is
arguably more vulnerable to disruption than any other: music.
AI-generated creative works—from orchestra performances to
heavy metal—are poised to suffuse our lives more thoroughly
than any other product of AI has done yet. The songs are likely
to blend into our streaming platforms, party and wedding playlists, soundtracks, and more, whether or not we notice who (or
what) made them.
For years, diffusion models have stirred debate in the visual-art
world about whether what they produce reflects true creation
or mere replication. Now this debate has come for music, an art
form that is deeply embedded in our experiences, memories,
and social lives. Music models can now create songs capable of
eliciting real emotional responses, presenting a stark example
of how difficult it’s becoming to define authorship and originality in the age of AI.
The courts are actively grappling with this murky territory.
Major record labels are suing the top AI music generators, alleging that diffusion models do little more than replicate human
art without compensation to artists. The model makers counter
that their tools are made to assist in human creation.
In deciding who is right, we’re forced to think hard about
our own human creativity. Is creativity, whether in artificial
neural networks or biological ones, merely the result of vast
statistical learning and drawn connections, with a sprinkling
of randomness? If so, then authorship is a slippery concept.
If not—if there is some distinctly human element to creativity—what is it? What does it mean to be moved by something
without a human creator? I had to wrestle with these questions
the first time I heard an AI-generated song that was genuinely
fantastic—it was unsettling to know that someone merely wrote
a prompt and clicked “Generate.” That predicament is coming
soon for you, too.
Diffusion AI models, and the machine-made music
they create, are complicating our ability to define
authorship and originality.
By James O’Donnell
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Illustrations by Stuart Bradford
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Making connections
After the Dartmouth conference, its participants went off in
different research directions to create the foundational technologies of AI. At the same time, cognitive scientists were following a 1950 call from J.P. Guilford, president of the American
Psychological Association, to tackle the question of creativity in
human beings. They came to a definition, first formalized in 1953
by the psychologist Morris Stein in the Journal of Psychology:
Creative works are both novel, meaning they present something
new, and useful, meaning they serve some purpose to someone.
Some have called for “useful”
to be replaced by “satisfying,”
and others have pushed for a
third criterion: that creative
things are also surprising.
Later, in the 1990s, the rise
of functional magnetic resonance imaging made it possible to study more of the neural
mechanisms underlying creativity in many fields, including music. Computational
methods in the past few years
have also made it easier to map
out the role that memory and
associative thinking play in
creative decisions.
What has emerged is less
a grand unified theory of how
a creative idea originates and
unfolds in the brain and more
an ever-growing list of powerful observations. We can
first divide the human creative process into phases, including
an ideation or proposal step, followed by a more critical and
evaluative step that looks for merit in ideas. A leading theory on
what guides these two phases is called the associative theory of
creativity, which posits that the most creative people can form
novel connections between distant concepts.
“It could be like spreading activation,” says Roger Beaty, a
researcher who leads the Cognitive Neuroscience of Creativity
Laboratory at Penn State. “You think of one thing; it just kind
of activates related concepts to whatever that one concept is.”
These connections often hinge specifically on semantic memory, which stores concepts and facts, as opposed to episodic
memory, which stores memories from a particular time and place.
Recently, more sophisticated computational models have been
used to study how people make connections between concepts
across great “semantic distances.” For example, the word apocalypse is more closely related to nuclear power than to celebration.
Studies have shown that highly creative people may perceive very
semantically distinct concepts as close together. Artists have been
found to generate word associations across greater distances than
non-artists. Other research has supported the idea that creative
people have “leaky” attention—that is, they often notice information that might not be particularly relevant to their immediate task.
Neuroscientific methods for evaluating these processes do not
suggest that creativity unfolds in a particular area of the brain.
“Nothing in the brain produces creativity like a gland secretes a
hormone,” Dean Keith Simonton, a leader in creativity research,
wrote in the Cambridge Handbook of the Neuroscience of Creativity.
The evidence instead points to a few dispersed networks of
activity during creative thought,
Beaty says—one to support
the initial generation of ideas
through associative thinking,
another involved in identifying
promising ideas, and another
for evaluation and modification.
A new study, led by researchers at Harvard Medical School
and published in February, suggests that creativity might even
involve the suppression of particular brain networks, like ones
involved in self-censorship.
So far, machine creativity—
if you can call it that—looks
quite different. Though at the
time of the Dartmouth conference AI researchers were interested in machines inspired by
human brains, that focus had
shifted by the time diffusion
models were invented, about
a decade ago.
The best clue to how they work is in the name. If you dip a
paintbrush loaded with red ink into a glass jar of water, the ink
will diffuse and swirl into the water seemingly at random, eventually yielding a pale pink liquid. Diffusion models simulate this
process in reverse, reconstructing legible forms from randomness.
For a sense of how this works for images, picture a photo of
an elephant. To train the model, you make a copy of the photo,
adding a layer of random black-and-white static on top. Make
a second copy and add a bit more, and so on hundreds of times
until the last image is pure static, with no elephant in sight. For
each image in between, a statistical model predicts how much
of the image is noise and how much is really the elephant. It
compares its guesses with the right answers and learns from
its mistakes. Over millions of these examples, the model gets
better at “de-noising” the images and connecting these patterns
to descriptions like “male Borneo elephant in an open field.”
Now that it’s been trained, generating a new image means
reversing this process. If you give the model a prompt, like “a
The results of Udio and
Suno so far suggest
there’s a sizable audience
of people who may not
care whether the music
they listen to is made by
humans or machines.
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happy orangutan in a mossy forest,” it generates an image of
random white noise and works backward, using its statistical
model to remove bits of noise step by step. At first, rough shapes
and colors appear. Details come after, and finally (if it works) an
orangutan emerges, all without the model “knowing” what an
orangutan is.
Musical images
The approach works much the same way for music. A diffusion
model does not “compose” a song the way a band might, starting
with piano chords and adding
vocals and drums. Instead, all
the elements are generated at
once. The process hinges on
the fact that the many complexities of a song can be depicted
visually in a single waveform,
representing the amplitude of a
sound wave plotted against time.
Think of a record player. By
traveling along a groove in a
piece of vinyl, a needle mirrors
the path of the sound waves
engraved in the material and
transmits it into a signal for the
speaker. The speaker simply
pushes out air in these patterns,
generating sound waves that
convey the whole song.
From a distance, a waveform
might look as if it just follows a
song’s volume. But if you were
to zoom in closely enough, you
could see patterns in the spikes and valleys, like the 49 waves per
second for a bass guitar playing a low G. A waveform contains
the summation of the frequencies of all different instruments
and textures. “You see certain shapes start taking place,” says
David Ding, cofounder of the AI music company Udio, “and that
kind of corresponds to the broad melodic sense.”
Since waveforms, or similar charts called spectrograms, can
be treated like images, you can create a diffusion model out of
them. A model is fed millions of clips of existing songs, each
labeled with a description. To generate a new song, it starts
with pure random noise and works backward to create a new
waveform. The path it takes to do so is shaped by what words
someone puts into the prompt.
Ding worked at Google DeepMind for five years as a senior
research engineer on diffusion models for images and videos, but
he left to found Udio, based in New York, in 2023. The company
and its competitor Suno, based in Cambridge, Massachusetts,
are now leading the race for music generation models. Both aim
to build AI tools that enable nonmusicians to make music. Suno
MJ25-feature_music.indd 39
is larger, claiming more than 12 million users, and raised a $125
million funding round in May 2024. The company has partnered
with artists including Timbaland. Udio raised a seed funding
round of $10 million in April 2024 from prominent investors
like Andreessen Horowitz as well as musicians Will.i.am and
Common.
The results of Udio and Suno so far suggest there’s a sizable
audience of people who may not care whether the music they listen to is made by humans or machines. Suno has artist pages for
creators, some with large followings, who generate songs entirely
with AI, often accompanied
by AI-generated images of
the artist. These creators are
not musicians in the conventional sense but skilled
prompters, creating work
that can’t be attributed to a
single composer or singer.
In this emerging space, our
normal definitions of authorship—and our lines between
creation and replication—all
but dissolve.
The music industry is
pushing back. Both companies were sued by major
record labels in June 2024,
and the lawsuits are ongoing. The labels, including
Universal and Sony, allege
that the AI models have been
trained on copyrighted music
“at an almost unimaginable
scale” and generate songs that “imitate the qualities of genuine
human sound recordings” (the case against Suno cites one ABBAadjacent song called “Prancing Queen,” for example).
Suno did not respond to requests for comment on the litigation, but in a statement responding to the case posted on
Suno’s blog in August, CEO Mikey Shulman said the company
trains on music found on the open internet, which “indeed
contains copyrighted materials.” But, he argued, “learning is
not infringing.”
A representative from Udio said the company would not
comment on pending litigation. At the time of the lawsuit, Udio
released a statement mentioning that its model has filters to ensure
that it “does not reproduce copyrighted works or artists’ voices.”
Complicating matters even further is guidance from the US
Copyright Office, released in January, that says AI-generated
works can be copyrighted if they involve a considerable amount
of human input. A month later, an artist in New York received
what might be the first copyright for a piece of visual art made
with the help of AI. The first song could be next.
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Novelty and mimicry
These legal cases wade into a gray area similar to one explored
by other court battles unfolding in AI. At issue here is whether
training AI models on copyrighted content is allowed, and
whether generated songs unfairly copy a human artist’s style.
But AI music is likely to proliferate in some form regardless
of these court decisions; YouTube has reportedly been in talks
with major labels to license their music for AI training, and
Meta’s recent expansion of its agreements with Universal Music
Group suggests that licensing for AI-generated music might be
on the table.
If AI music is here to stay,
will any of it be any good?
Consider three factors: the
training data, the diffusion
model itself, and the prompting. The model can only be as
good as the library of music it
learns from and the descriptions of that music, which must
be complex to capture it well.
A model’s architecture then
determines how well it can use
what’s been learned to generate songs. And the prompt you
feed into the model—as well as
the extent to which the model
“understands” what you mean
by “turn down that saxophone,”
for example—is pivotal too.
Arguably the most important issue is the first: How
extensive and diverse is the
training data, and how well is it labeled? Neither Suno nor Udio
has disclosed what music has gone into its training set, though
these details will likely have to be disclosed during the lawsuits.
Udio says the way those songs are labeled is essential to the
model. “An area of active research for us is: How do we get more
and more refined descriptions of music?” Ding says. A basic
description would identify the genre, but then you could also
say whether a song is moody, uplifting, or calm. More technical
descriptions might mention a two-five-one chord progression
or a specific scale. Udio says it does this through a combination
of machine and human labeling.
“Since we want to target a broad range of target users, that
also means that we need a broad range of music annotators,”
he says. “Not just people with music PhDs who can describe
the music on a very technical level, but also music enthusiasts
who have their own informal vocabulary for describing music.”
Competitive AI music generators must also learn from a constant supply of new songs made by people, or else their outputs
will be stuck in time, sounding stale and dated. For this, today’s
MJ25-feature_music.indd 40
AI-generated music relies on human-generated art. In the future,
though, AI music models may train on their own outputs, an
approach being experimented with in other AI domains.
Because models start with a random sampling of noise, they
are nondeterministic; giving the same AI model the same prompt
will result in a new song each time. That’s also because many
makers of diffusion models, including Udio, inject additional
randomness through the process—essentially taking the waveform generated at each step and distorting it ever so slightly in
hopes of adding imperfections that serve to make the output
more interesting or real. The
organizers of the Dartmouth
conference themselves recommended such a tactic back
in 1956.
According to Udio cofounder and chief operating
officer Andrew Sanchez, it’s
this randomness inherent in
generative AI programs that
comes as a shock to many
people. For the past 70 years,
computers have executed
deterministic programs:
Give the software an input
and receive the same response
every time.
“Many of our artists partners will be like, ‘Well, why
does it do this?’” he says.
“We’re like, well, we don’t
really know.” The generative
era requires a new mindset,
even for the companies creating it: that AI programs can be
messy and inscrutable.
Is the result creation or simply replication of the training
data? Fans of AI music told me we could ask the same question
about human creativity. As we listen to music through our youth,
neural mechanisms for learning are weighted by these inputs,
and memories of these songs influence our creative outputs.
In a recent study, Anthony Brandt, a composer and professor
of music at Rice University, pointed out that both humans and
large language models use past experiences to evaluate possible
future scenarios and make better choices.
Indeed, much of human art, especially in music, is borrowed. This often results in litigation, with artists alleging
that a song was copied or sampled without permission. Some
artists suggest that diffusion models should be made more
transparent, so we could know that a given song’s inspiration
is three parts David Bowie and one part Lou Reed. Udio says
there is ongoing research to achieve this, but right now, no
one can do it reliably.
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For great artists, “there is that combination of novelty and
influence that is at play,” Sanchez says. “And I think that that’s
something that is also at play in these technologies.”
But there are lots of areas where attempts to equate human
neural networks with artificial ones quickly fall apart under scrutiny. Brandt carves out one domain where he sees human creativity clearly soar above its machine-made counterparts: what he
calls “amplifying the anomaly.” AI models operate in the realm
of statistical sampling. They do not work by emphasizing the
exceptional but, rather, by reducing errors and finding probable
patterns. Humans, on the other
hand, are intrigued by quirks.
“Rather than being treated as
oddball events or ‘one-offs,’”
Brandt writes, the quirk “permeates the creative product.”
He cites Beethoven’s
decision to add a jarring offkey note in the last movement of his Symphony no. 8.
“Beethoven could have left it at
that,” Brandt says. “But rather
than treating it as a one-off,
Beethoven continues to reference this incongruous event in
various ways. In doing so, the
composer takes a momentary
aberration and magnifies its
impact.” One could look to similar anomalies in the backward
loop sampling of late Beatles
recordings, pitched-up vocals
from Frank Ocean, or the incorporation of “found sounds,” like recordings of a crosswalk signal
or a door closing, favored by artists like Charlie Puth and by Billie
Eilish’s producer Finneas O’Connell.
If a creative output is indeed defined as one that’s both novel
and useful, Brandt’s interpretation suggests that the machines
may have us matched on the second criterion while humans
reign supreme on the first.
To explore whether that is true, I spent a few days playing
around with Udio’s model. It takes a minute or two to generate
a 30-second sample, but if you have paid versions of the model
you can generate whole songs. I decided to pick 12 genres, generate a song sample for each, and then find similar songs made
by people. I built a quiz to see if people in our newsroom could
spot which songs were made by AI.
The average score was 46%. And for a few genres, especially
instrumental ones, listeners were wrong more often than not.
When I watched people do the test in front of me, I noticed that
the qualities they confidently flagged as a sign of composition by
AI—a fake-sounding instrument, a weird lyric—rarely proved
them right. Predictably, people did worse in genres they were
less familiar with; some did okay on country or soul, but many
stood no chance against jazz, classical piano, or pop. Beaty, the
creativity researcher, scored 66%, while Brandt, the composer,
finished at 50% (though he answered correctly on the orchestral
and piano sonata tests).
Remember that the model doesn’t deserve all the credit here;
these outputs could not have been created without the work of
human artists whose work was in the training data. But with
just a few prompts, the model generated songs that few people
would pick out as machinemade. A few could easily have
been played at a party without raising objections, and I
found two I genuinely loved,
even as a lifelong musician
and generally picky music person. But sounding real is not
the same thing as sounding
original. The songs did not feel
driven by oddities or anomalies—certainly not on the level
of Beethoven’s “jump scare.”
Nor did they seem to bend
genres or cover great leaps
between themes. In my test,
people sometimes struggled
to decide whether a song was
AI-generated or simply bad.
How much will this matter
in the end? The courts will
play a role in deciding whether
AI music models serve up replications or new creations—and how artists are compensated
in the process—but we, as listeners, will decide their cultural
value. To appreciate a song, do we need to picture a human artist
behind it—someone with experience, ambitions, opinions? Is a
great song no longer great if we find out it’s the product of AI?
Sanchez says people may wonder who is behind the music.
But “at the end of the day, however much AI component, however
much human component, it’s going to be art,” he says. “And people are going to react to it on the quality of its aesthetic merits.”
In my experiment, though, I saw that the question really
mattered to people—and some vehemently resisted the idea of
enjoying music made by a computer model. When one of my test
subjects instinctively started bobbing her head to an electro-pop
song on the quiz, her face expressed doubt. It was almost as if she
was trying her best to picture a human rather than a machine as
the song’s composer. “Man,” she said, “I really hope this isn’t AI.”
It was.
Is the result creation
or simply replication
of the training data?
We could ask the
same question about
human creativity.
MJ25-feature_music.indd 41
James O’Donnell covers artificial intelligence at MIT
Technology Review.
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A nuclear
explosion
might eventually
be Earth’s only way to
protect itself from a
dangerous
space rock.
But preparing for
that day without testing
nukes in space means
getting
creative.
A T O M S F O R ASTEROIDS
By Robin George Andrews
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Illustrations by MCKIBILLO
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44
One day, in the near or far future, an asteroid about the
length of a football stadium will find itself on a collision
course with Earth. If we are lucky, it will land in the middle of the vast ocean, creating a good-size but innocuous
tsunami, or in an uninhabited patch of desert. But if it
has a city in its crosshairs, one of the worst natural disasters in modern times will unfold. As the asteroid steams
through the atmosphere, it will begin to fragment—
but the bulk of it will likely make it
to the ground in just a few seconds,
instantly turning anything solid into
a fluid and excavating a huge impact
crater in a heartbeat. A colossal blast
wave, akin to one unleashed by a
large nuclear weapon, will explode
from the impact site in every direction. Homes dozens of miles away
will fold like cardboard. Millions of
people could die.
Fortunately for all 8 billion of us,
planetary defense—the science of
preventing asteroid impacts—is
a highly active field of research.
Astronomers are watching the
skies, constantly on the hunt for
new near-Earth objects that might
pose a threat. And others are actively
working on developing ways to prevent a collision should we find an
asteroid that seems likely to hit us.
We already know that at least
one method works: ramming the
rock with an uncrewed spacecraft
to push it away from Earth. In
September 2022, NASA’s Double
Asteroid Redirection Test, or DART,
showed it could be done when a
semiautonomous spacecraft the
size of a small car, with solar panel
wings, was smashed into an (innocuous) asteroid named Dimorphos at
14,000 miles per hour, successfully
changing its orbit around a larger
asteroid named Didymos.
But there are circumstances in
which giving an asteroid a physical shove might not be enough to
protect the planet. If that’s the case,
MJ25-feature_asteroids.indd 44
we could need another method, one
that is notoriously difficult to test in
real life: a nuclear explosion.
Scientists have used computer
simulations to explore this potential method of planetary defense.
But in an ideal world, researchers
would ground their models with
cold, hard, practical data. Therein
lies a challenge. Sending a nuclear
weapon into space would violate
international laws and risk inflaming
political tensions. What’s more, it
could do damage to Earth: A rocket
malfunction could send radioactive
debris into the atmosphere.
Over the last few years, however, scientists have started to
devise some creative ways around
this experimental limitation.
The effort began in 2023, with a
team of scientists led by Nathan
Moore, a physicist and chemical
engineer at the Sandia National
Laboratories in Albuquerque, New
Mexico. Sandia is a semi-secretive
site that serves as the engineering
arm of America’s nuclear weapons
program. And within that complex
lies the Z Pulsed Power Facility, or
Z machine, a cylindrical metallic labyrinth of warning signs and wiring.
It’s capable of summoning enough
energy to melt diamond.
The researchers reckoned they
could use the Z machine to re-create
the x-ray blast of a nuclear weapon—
the radiation that would be used to
knock back an asteroid—on a very
small and safe scale.
It took a while to sort out the
details. But by July 2023, Moore and
his team were ready. They waited anxiously inside a control room, monitoring the thrumming contraption from
afar. Inside the machine’s heart were
two small pieces of rock, stand-ins for
asteroids, and at the press of a button,
a maelstrom of x-rays would thunder
toward them. If they were knocked
back by those x-rays, it would prove
something that, until now, was purely
theoretical: You can deflect an asteroid from Earth using a nuke.
This experiment “had never been
done before,” says Moore. But if it
About 25,000 asteroids
more than 460 feet
long—a size range that
starts with midsize “city
killers” and goes up in
impact from there—are
thought to exist close to
Earth. Just under half of
them have been found.
succeeded, its data would contribute to the safety of everyone on the
planet. Would it work?
Monoliths and
rubble piles
Asteroid impacts are a natural disaster like any other. You shouldn’t lose
sleep over the prospect, but if we get
unlucky, an errant space rock may
rudely ring Earth’s doorbell. “The
probability of an asteroid striking
Earth during my lifetime is very small.
But what if one did? What would we
do about it?” says Moore. “I think
that’s worth being curious about.”
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45
Forget about the gigantic asteroids you know from Hollywood
blockbusters. Space rocks over twothirds of a mile (about one kilometer) in diameter—those capable of
imperiling civilization—are certainly out there, and some hew close
to Earth’s own orbit. But because
these asteroids are so elephantine,
astronomers have found almost all
of them already, and none pose an
impact threat.
Rather, it’s asteroids a size range
down—those upwards of 460 feet
(140 meters) long—that are of paramount concern. About 25,000 of
those are thought to exist close to
our planet, and just under half have
been found. The day-to-day odds of
an impact are extremely low, but
even one of the smaller ones in that
size range could do significant damage if it found Earth and hit a populated area—a capacity that has led
astronomers to dub such midsize
asteroids “city killers.”
If we find a city killer that looks
likely to hit Earth, we’ll need a way
to stop it. That could be technology
to break or “disrupt” the asteroid
into fragments that will either miss
the planet entirely or harmlessly
ignite in the atmosphere. Or it could
be something that can deflect the
asteroid, pushing it onto a path that
will no longer intersect with our
blue marble.
Because disruption could accidentally turn a big asteroid into
multiple smaller, but still deadly,
shards bound for Earth, it’s often
considered to be a strategy of last
resort. Deflection is seen as safer
and more elegant. One way to
achieve it is to deploy a spacecraft
known as a kinetic impactor—a
battering ram that collides with an
asteroid and transfers its momentum to the rocky interloper, nudging
it away from Earth. NASA’s DART
mission demonstrated that this can
work, but there are some important
MJ25-feature_asteroids.indd 45
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46
caveats: You need to deflect the
asteroid years in advance to make
sure it completely misses Earth, and
asteroids that we spot too late—or
that are too big—can’t be swatted
away by just one DART-like mission.
Instead, you’d need several kinetic
impactors—maybe many of them—
to hit one side of the asteroid perfectly each time in order to push it
far enough to save our planet. That’s
a tall order for orbital mechanics,
and not something space agencies
may be willing to gamble on.
In that case, the best option
might instead be to detonate a
nuclear weapon next to the asteroid.
This would irradiate one hemisphere
of the asteroid in x-rays, which in
a few millionths of a second would
violently shatter and vaporize the
rocky surface. The stream of debris
spewing out of that surface and
into space would act like a rocket,
pushing the asteroid in the opposite direction. “There are scenarios
where kinetic impact is insufficient,
and we’d have to use a nuclear explosive device,” says Moore.
This idea isn’t new. Several
decades ago, Peter Schultz, a
planetary geologist and impacts
expert at Brown University, was
giving a planetary defense talk at
the Lawrence Livermore National
Laboratory in California, another
American lab focused on nuclear
deterrence and nuclear physics
research. Afterwards, he recalls,
none other than Edward Teller, the
father of the hydrogen bomb and
a key member of the Manhattan
Project, invited him into his office
for a chat. “He wanted to do one
of these near-Earth-asteroid flybys and wanted to test the nukes,”
Schultz says. What, he wondered,
would happen if you blasted an
asteroid with a nuclear weapon’s
x-rays? Could you forestall a spaceborne disaster using weapons of
mass destruction?
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But Teller’s dream wasn’t fulfilled—and it’s unlikely to become
a reality anytime soon. The United
Nations’ 1967 Outer Space Treaty
states that no nation can deploy or
use nuclear weapons off-world (even
if it’s not clear how long certain
spacefaring nations will continue
to adhere to that rule).
Even raising the possibility of
using nukes to defend the planet
can be tricky. “There’re still many
folks that don’t want to talk about
it at all … even if that were the
only option to prevent an impact,”
says Megan Bruck Syal, a physicist
and planetary defense researcher
at Lawrence Livermore. Nuclear
weapons have long been a sensitive
subject, and with relations between
several nuclear nations currently at
a new nadir, anxiety over the subject
is understandable.
But in the US, there are groups
of scientists who “recognize that
we have a special responsibility as a
spacefaring nation and as a nuclearcapable nation to look at this,” Syal
says. “It isn’t our preference to use
a nuclear explosive, of course. But
we are still looking at it, in case it’s
needed.”
But how?
Mostly, researchers have turned
to the virtual world, using supercomputers at various US laboratories
to simulate the asteroid-agitating
physics of a nuclear blast. To put it
mildly, “this is very hard,” says Mary
Burkey, a physicist and planetary
defense researcher at Lawrence
Livermore. You cannot simply flick a
switch on a computer and get immediate answers. “When a nuke goes
off in space, there’s just x-ray light
that’s coming out of it. It’s shining
on the surface of your asteroid, and
you’re tracking those little photons
penetrating maybe a tiny little bit
into the surface, and then somehow you have to take that micrometer worth of resolution and then
The Z Pulsed Power Facility, or Z machine, at
Sandia National Laboratories in Albuquerque,
New Mexico, concentrates electricity into short
bursts of intense energy that can be used to
create x-rays and gamma rays and compress matter
to high densities.
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RANDY MONTOYA/SANDIA NATIONAL LABORATORY
47
propagate it out onto something that
might be on the order of hundreds
of meters wide, watching that shock
wave propagate and then watching
fragments spin off into space. That’s
four different problems.”
Mimicking the physics of x-ray
rock annihilation with as much verisimilitude as possible is difficult
work. But recent research using
these high-fidelity simulations does
suggest that nukes are an effective
planetary defense tool for both disruption and deflection. The thing is,
though, no two asteroids are alike;
each is mechanically and geologically unique, meaning huge uncertainties remain. A more monolithic
asteroid might respond in a straightforward way to a nuclear deflection
campaign, whereas a rubble pile
asteroid—a weakly bound fleet of
boulders barely held together by
their own gravity—might respond
in a chaotic, uncontrollable way. Can
you be sure the explosion wouldn’t
accidentally shatter the asteroid,
turning a cannonball into a hail of
bullets still headed for Earth?
Simulations can go a long way
toward answering these questions,
but they remain virtual re-creations
of reality, with built-in assumptions.
“Our models are only as good as
the physics that we understand
and that we put into them,” says
Angela Stickle, a hypervelocity impact physicist at the Johns
Hopkins University Applied Physics
Laboratory in Maryland. To make
sure the simulations are reproducing the correct physics and
delivering realistic data, physical
experiments are needed to ground
them.
Researchers studying kinetic
impactors can get that sort of realworld data. Along with DART, they
can use specialized cannons—
like the Vertical Gun Range at
NASA’s Ames Research Center
in California—to fire all sorts of
MJ25-feature_asteroids.indd 47
projectiles at meteorites. In doing
so, they can find out how tough or
fragile asteroid shards can be, effectively reproducing a kinetic impact
mission on a small scale.
Battle-testing nuke-based asteroid defense simulations is another
matter. Re-creating the physics of
these confrontations on a small scale
was long considered to be exceedingly difficult. Fortunately, those
keen on fighting asteroids are as
persistent as they are creative—and
several teams, including Moore’s
at Sandia, think they have come up
with a solution.
Every
firing
of the
Z
machine
carries
the
energy
of more than
1,000
lightning
bolts,
and each
shot lasts
a few
millionths
of a
second.
X-ray scissors
The prime mission of Sandia, like
that of Lawrence Livermore, is to
help maintain the nation’s nuclear
weapons arsenal. “It’s a national
security laboratory,” says Moore.
“Planetary defense affects the entire
planet,” he adds—making it, by
default, a national security issue
as well. And that logic, in part, persuaded the powers that be in July
2022 to try a brand-new kind of
experiment. Moore took charge of
the project in January 2023—and
with the shot scheduled for the summer, he had only a few months to
come up with the specific plan for
the experiment. There was “lots
of scribbling on my whiteboard,
running computer simulations,
and getting data to our engineers
to design the test fixture for the
several months it would take to get
all the parts machined and assembled,” he says.
Although there were previous
and ongoing experiments that
showered asteroid-like targets with
x-rays, Moore and his team were
frustrated by one aspect of them.
Unlike actual asteroids floating
freely in space, the micro-asteroids
on Earth were fixed in place. To
truly test whether x-rays could
deflect asteroids, targets would have
to be suspended in a vacuum—and
it wasn’t immediately clear how that
could be achieved.
Generating the nuke-like x-rays
was the easy part, because Sandia
had the Z machine, a hulking
mass of diodes, pipes, and wires
interwoven with an assortment of
walkways that circumnavigate a
vacuum chamber at its core. When
it’s powered up, electrical currents
are channeled into capacitors—
and, when commanded, blast that
energy at a target or substance to
create radiation and intense magnetic pressures.
Flanked by klaxons and flashing
lights, it’s an intimidating sight. “It’s
the size of a building—about three
stories tall,” says Moore. Every firing
of the Z machine carries the energy
of more than 1,000 lightning bolts,
and each shot lasts a few millionths
of a second: “You can’t even blink
that fast.” The Z machine is named
for the axis along which its energetic
particles cascade, but the Z could
easily stand for “Zeus.”
The original purpose of the
Z machine, whose first form was
built half a century ago, was nuclear
fusion research. But over time, it’s
been tinkered with, upgraded, and
used for all kinds of science. “The
Z machine has been used to compress matter to the same densities
[you’d find at] the centers of planets.
And we can do experiments like that
to better understand how planets
form,” Moore says, as an example.
And the machine’s preternatural
energies could easily be used to
generate x-rays—in this case, by
electrifying and collapsing a cloud
of argon gas.
“The idea of studying asteroid
deflection is completely different for
us,” says Moore. And the machine
“fires just once a day,” he adds, “so all
the experiments are planned more
than a year in advance.” In other
words, the researchers had to be
4/1/25 3:5 PM
48
near certain their one experiment
would work, or they would be in
for a long wait to try again—if they
were permitted a second attempt.
For some time, they could not
figure out how to suspend their
micro-asteroids. But eventually,
they found a solution: Two incredibly thin bits of aluminum foil would
hold their targets in place within
the Z machine’s vacuum chamber.
When the x-ray blast hit them and
the targets, the pieces of foil would
be instantly vaporized, briefly leaving the targets suspended in the
chamber and allowing them to be
pushed back as if they were in space.
“It’s like you wave your magic wand
and it’s gone,” Moore says of the foil.
He dubbed this technique “x-ray
scissors.”
In July 2023, after considerable planning, the team was ready.
Within the Z machine’s vacuum
chamber were two fingernail-size
targets—a bit of quartz and some
fused silica, both frequently found
on real asteroids. Nearby, a pocket
of argon gas swirled away. Satisfied
that the gigantic gizmo was ready,
everyone left and went to stand in
the control room. For a moment, it
was deathly quiet.
Stand by.
Fire.
It was over before their ears could
even register a metallic bang. A tempest of electricity shocked the argon
gas cloud, causing it to implode; as
it did, it transformed into a plasma
and x-rays screamed out of it, racing
toward the two targets in the chamber. The foil vanished, the surfaces
of both targets erupted outward as
supersonic sprays of debris, and the
targets flew backward, away from
the x-rays, at 160 miles per hour.
Moore wasn’t there. “I was in
Spain when the experiment was
run, because I was celebrating
my anniversary with my wife, and
there was no way I was going to
MJ25-feature_asteroids.indd 48
miss that,” he says. But just after
the Z machine was fired, one of his
colleagues sent him a very concise
text: IT WORKED.
“We knew right away it was a
huge success,” says Moore. The
implications were immediately clear.
The experimental setup was complex, but they were trying to achieve
something extremely fundamental:
a real-world demonstration that a
nuclear blast could make an object
in space move.
Patrick King, a physicist at the
Johns Hopkins University Applied
Physics Laboratory, was impressed.
Previously, pushing back objects
using x-ray vaporization had been
extremely difficult to demonstrate
in the lab. “They were able to
get a direct measurement of that
momentum transfer,” he says, calling the x-ray scissors an “elegant”
technique.
Sandia’s work took many in
the community by surprise. “The
Z machine experiment was a bit of a
newcomer for the planetary defense
field,” says Burkey. But she notes that
we can’t overinterpret the results.
It isn’t clear, from the deflection
of the very small and rudimentary
asteroid-like targets, how much a
genuine nuclear explosion would
deflect an actual asteroid. As ever,
more work is needed.
King leads a team that is also
working on this question. His
NASA-funded project involves
the Omega Laser Facility, a complex based at the University of
Rochester in upstate New York.
Omega can generate x-rays by
firing powerful lasers at a target
within a specialized chamber. Upon
being irradiated, the target generates an x-ray flash, similar to the
one produced during a nuclear
explosion in space, which can
then be used to bombard various
objects—in this case, some Earth
rocks acting as asteroid mimics,
and (crucially) some bona fide
meteoritic material too.
King’s Omega experiments have
tried to answer a basic question:
“How much material actually gets
removed from the surface?” says
King. The amount of material that
flies off the pseudo-asteroids, and
the vigor with which it’s removed,
will differ from target to target. The
hope is that these results—which
the team is still considering—
will hint at how different types of
asteroids will react to being nuked.
Although experiments with Omega
cannot produce the kickback seen in
“We’re genuinely
looking at this from
the standpoint of
‘This is a technology
that could save lives.’”
the Z machine, King’s team has used
a more realistic and diverse series
of targets and blasted them with
x-rays hundreds of times. That, in
turn, should clue us in to how effectively, or not, actual asteroids would
be deflected by a nuclear explosion.
“I wouldn’t say one [experiment]
has definitive advantages over the
other,” says King. “Like many things
in science, each approach can yield
insight along different ‘axes,’ if you
will, and no experimental setup
gives you the whole picture.”
Experiments like Moore’s and
King’s may sound technologically
4/1/25 3:5 PM
49
baroque—a bit like lightning-fast
Rube Goldberg machines overseen
by wizards. But they are likely the first
in a long line of increasingly sophisticated tests. “We’ve just scratched the
surface of what we can do,” Moore
says. As with King’s experiments,
Moore hopes to place a variety of
materials in the Z machine, including
targets that can stand in for the wetter, more fragile carbon-rich asteroids
that astronomers commonly see in
near-Earth space. “If we could get our
hands on real asteroid material, we’d
do it,” he says. And it’s expected that
all this experimental data will be fed
back into those nuke-versus-asteroid
computer simulations, helping to
verify the virtual results.
Although these experiments are
perfectly safe, planetary defenders
remain fully cognizant of the taboo
around merely discussing the use
of nukes for any reason—even if
that reason is potentially saving the
world. “We’re genuinely looking at
this from the standpoint of ‘This is
a technology that could save lives,’”
King says.
Inevitably, Earth will be imperiled
by a dangerous asteroid. And the
hope is that when that day arrives,
it can be dealt with using something
other than a nuke. But comfort should
be taken from the fact that scientists
are researching this scenario, just in
case it’s our only protection against
the firmament. “We are your taxpayer
dollars at work,” says Burkey.
There’s still some way to go
before they can be near certain that
this asteroid-stopping technique will
succeed. Their progress, though,
belongs to everyone. “Ultimately,”
says Moore, “we all win if we solve
this problem.”
Robin George Andrews is
an award-winning science
journalist based in London and
the author, most recently,
of How to Kill an Asteroid:
The Real Science of Planetary
Defense.
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50
Artificial intelligence is painting
pictures, writing novels,
making videos, and composing
symphonies. Can it push the
limits of what we build?
By Allison Arieff
Karl Daubmann
College of Architecture and Design
at Lawrence Technological
University
“The way I have been working with generative
AI has been to mix multiple images together
from vast libraries I have from architecture,
construction, history, fashion, biology,
etc.,” says Daubmann. “Very often the new
synthetic image that comes from a tool like
Midjourney or Stable Diffusion feels new,
infused by each of the multiple tools but rarely
completely derived from them.” Daubmann’s
Instagram account “Robot Historian,” a daily
account of a unique postindustrial, postrobotic landscape, has become a document
for tracking the evolution of generative AI.
MJ25-feature_architecture.indd 50
Architecture often assumes a binary
between built projects and theoretical
ones. What physics allows in actual buildings, after all, is vastly different from what
architects can imagine and design (often
referred to as “paper architecture”). That
imagination has long been supported and
enabled by design technology, but the latest advancements in artificial intelligence
have prompted a surge in the theoretical.
“Transductions: Artificial Intelligence
in Architectural Experimentation,” a recent
exhibition at the Pratt Institute in Brooklyn,
brought together works from over 30 practitioners exploring the experimental, generative, and collaborative potential of artificial
intelligence to open up new areas of architectural inquiry—something they’ve been
working on for a decade or more, since long
before AI became mainstream. Architects
and exhibition co-curators Jason VigneriBeane, Olivia Vien, Stephen Slaughter, and
Hart Marlow explain that the works in
“Transductions” emerged out of feedback
loops among architectural discourses, techniques, formats, and media that range from
imagery, text, and animation to mixed-reality
media and fabrication. The aim isn’t to present projects that are going to break ground
anytime soon; architects already know how
to build things with the tools they have.
Instead, the show attempts to capture this
very early stage in architecture’s exploratory
engagement with AI.
Technology has long enabled architecture to push the limits of form and function. As early as 1963, Sketchpad, one of
the first architectural software programs,
allowed architects and designers to move
and change objects on screen. Rapidly,
traditional hand drawing gave way to an
ever-expanding suite of programs—Revit,
SketchUp, and BIM, among many others—
that helped create floor plans and sections,
track buildings’ energy usage, enhance sustainable construction, and aid in following
building codes, to name just a few uses.
The architects exhibiting in “Transductions” view newly evolving forms of AI
“like a new tool rather than a professionending development,” says Vigneri-Beane,
despite what some of his peers fear about
the technology. He adds, “I do appreciate
that it’s a somewhat unnerving thing for
people, [but] I feel a familiarity with the
rhetoric.”
After all, he says, AI doesn’t just do the
job. “To get something interesting and
worth saving in AI, an enormous amount
of time is required,” he says. “My architectural vocabulary has gotten much more
precise and my visual sense has gotten an
incredible workout, exercising all these
muscles which have atrophied a little bit.”
Vien agrees: “I think these are
extremely powerful tools for an architect
and designer. Do I think it’s the entire
future of architecture? No, but I think it’s
a tool and a medium that can expand the
long history of mediums and media that
architects can use not just to represent
their work but as a generator of ideas.”
Allison Arieff is editorial director
of MIT Technology Review.
COURTESY OF KARL DAUBMANN
Generating
architecture
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GUTTER CREDIT HERE
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GUTTER CREDIT HERE
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COURTESY OF THE ARTISTS
53
Opposite:
Above:
Andrew Kudless
Jason Vigneri-Beane
Hines College of Architecture and Design
Pratt Institute
This image, part of the Urban Resolution series, shows how the Stable
Diffusion AI model “is unable to focus on constructing a realistic image and
instead duplicates features that are prominent in the local latent space,”
Kudless says. “For example, for [the prompt] ‘A street in [Tokyo],’ advertising
signs and utility wires make up most of the image, while for ‘A street in [Los
Angeles],’ it is palm trees and cars that dominate.”
“These images are from a larger series on cyborg ecologies that have to do
with co-creating with machines to imagine [other] machines,” says VigneriBeane. “Or machines to imagine the machines that would weave synthetic
foliation. Or machines that would become massive floating terraforming
infrastructures. Or drones that would be weaving and 3D-printing synthetic
flora. It fuels scenario thinking. I might refer to these as cryptomegafauna—
infrastructural robots operating at an architectural scale.”
MJ25-feature_architecture.indd 53
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54
Robert Lee Brackett III and Duks Koschitz
Pratt Institute
interpret our knowledge through sketches and
guided image generation. The resulting images
are a response to the temporary and ephemeral
nature of pneumatic structures with a desire
to capture elegance and lightness in a more
permanent material.”
GUTTER CREDIT HERE
“We face many challenges of space and
resources that constrain our ambition to test
inflatable concrete architecture at full scale,”
explains Brackett, “so we were curious if
Stable Diffusion’s Juggernaut XL models could
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55
Martin Summers
University of Kentucky College of Design
Summers turned to the iconic midcentury modern Case Study House
program for inspiration. “Most AI is racing to emulate reality,” he explains. “I
prefer to revel in the hallucinations and misinterpretations like glitches and
the sublogic they reveal present in a mediated reality.”
Jason Lee
MJ25-feature_architecture.indd 55
COURTESY OF THE ARTISTS
GUTTER CREDIT HERE
Pratt Institute
Lee typically uses AI “to generate iterations or high-resolution sketches,”
he says. “So often, I am using a visual asset that I have created as a seed
or weight to guide the image model to create new variations with additional
language prompts. I am also using it to experiment with how much realism
one can incorporate with more abstract representation methods.”
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COURTESY OF THE ARTISTS
56
Above:
Opposite:
Olivia Vien
Robert Lee Brackett III
Pratt Institute
Pratt Institute
For the series Imprinting Grounds, Vien created images digitally and fed them
into Midjourney. “It riffs on the ideas of damask textile patterns in a more
digital realm—a hybrid between hyper-ornate classic textile patterns and
more contemporary forms and shapes,” she says.
“While new software raises concerns about the absence of traditional tools
like hand drawing and modeling, I view these technologies as collaborators
rather than replacements,” Brackett says. “Emerging architects and designers are occupying a threshold where they must learn from both worlds to
create the future of architectural experimentation.”
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GUTTER CREDIT HERE
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58
How a 1980s toy robot arm inspired modern robotics.
The
story
of
the
Arm
atro
n
By Jon Keegan
Photographs by Jim Golden
As a child of an electronic engineer, I
spent a lot of time in our local Radio
Shack as a kid. While my dad was
locating capacitors and resistors, I
was in the toy section. It was there,
in 1984, that I discovered the best
toy of my childhood: the Armatron
robotic arm.
Described as a “robot-like arm
to aid young masterminds in scientific and laboratory experiments,”
it was the rare toy that lived up to
the hype printed on the front of
the box. This was a legit robotic
arm. You could rotate the arm to
spin around its base, tilt it up and
down, bend it at the “elbow” joint,
rotate the “wrist,” and open and
close the bright-orange articulated
hand in elegant chords of movement, all using only the twistable
twin joysticks.
Anyone who played with this
toy will also remember the sound it
made. Once you slid the power button to the On position, you heard a
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constant whirring sound of plastic
gears turning and twisting. And if
you tried to push it past its boundaries, it twitched and protested with a
jarring “CLICK … CLICK … CLICK.”
It wasn’t just kids who found the
Armatron so special. It was featured
on the cover of the November/
December 1982 issue of Robotics
Age magazine, which noted that
the $31.95 toy (about $96 today)
had “capabilities usually found only
in much more expensive experimental arms.”
A few years ago I found my
Armatron, and when I opened the
case to get it working again, I was
startled to find that other than the
compartment for the pair of D-cell
batteries, a switch, and a tiny threevolt DC motor, this thing was totally
devoid of any electronic components. It was purely mechanical.
Later, I found the patent drawings
for the Armatron online and saw how
incredibly complex the schematics
of the gearbox were. This design was
the work of a genius—or a madman.
The man behind the arm
A drawing from the
patent application
for the Armatron
robotic arm.
MJ25-back_armatron.indd 60
I needed to know the story of this
toy. I reached out to the manufacturer, Tomy (now known as Takara
Tomy), which has been in business in Japan for over 100 years.
It put me in touch with Hiroyuki
Watanabe, a 69-year-old engineer
and toy designer living in Tokyo.
He’s retired now, but he worked at
Tomy for 49 years, building many
classic handheld electronic toys
of the ’80s, including Blip, Digital
Diamond, Digital Derby, and Missile
Strike. Watanabe’s name can be
found on 44 patents, and he was
involved in bringing between 50 and
60 products to market. Watanabe
answered emailed questions via
video, and his responses were translated from Japanese.
“I didn’t have a period where I
studied engineering professionally.
Instead, I enrolled in what Japan
would call a technical high school
that trains technical engineers, and
I actually [entered] the electrical
department there,” he told me.
Afterward, he worked at Komatsu
Manufacturing—because, he said,
he liked bulldozers. But in 1974, he
saw that Tomy was hiring, and he
wanted to make toys. “I was told
that it was the No. 1 toy company in
Japan, so I decided [it was worth a
look],” he said. “I took a night train
from Tohoku to Tokyo to take a job
exam, and that’s how I ended up
joining the company.”
The inspiration for the Armatron
came from a newspaper clipping
that Watanabe’s boss brought to
him one day. “It showed an image
of a [mechanical arm] holding an
egg with three fingers. I think we
started out thinking, ‘This is where
things are heading these days, so
let’s make this,’” he recalled.
As the lead of a small team,
Watanabe briefly turned his attention to another project, and by the
time he returned to the robotic arm,
the team had a prototype. But it was
quite different from the Armatron’s
final form. “The hand stuck out from
the main body to the side and could
only move about 90 degrees. The
control panel also had six movement
positions, and they were switched
using six switches. I personally
didn’t like that,” said Watanabe. So
he went back to work.
Watanabe’s breakthrough was
inspired by the radio-controlled
helicopters he operated as a hobby.
Holding up a radio remote controller
with dual joystick controls, he told
me, “This stick operation allows you
to perform four movements with two
arms, but I thought that if you twist
this part, you can use six movements.”
“I had always wanted to create a system that could rotate 360
degrees, so I thought about how to
make that system work,” he added.
COURTESY OF TAKARA TOMY
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62
Watanabe stressed that while
he is listed as the Armatron’s primary inventor, it was a team effort.
A designer created the case, colors,
and logo, adding touches to mimic
features seen on industrial robots of
the time, such as the rubber tubes
(which are just for looks).
When the Armatron first came
out, in 1981, robotics engineers
started contacting Watanabe. “I
wasn’t so much hearing from people at toy stores, but rather from
researchers at university laboratories, factories, and companies that
were making industrial robots,” he
said. “They were quite encouraging,
and we often talked together.”
The long reach of the
robot at Radio Shack
The bold look and function of
Armatron made quite an impression on many young kids who would
one day have a career in robotics.
One of them was Adam Burrell, a
mechanical design engineer who has
been building robots for 15 years at
Boston Dynamics, including Petman,
the YouTube-famous Atlas, and the
dog-size quadruped called Spot.
Burrell grew up a few blocks
away from a Radio Shack in New
York City. “If I was going to the
subway station, we would walk right
by Radio Shack. I would stop in and
play with it and set the timer, do
the challenges,” he says. “I know it
was a toy, but that was a real robot.”
The Armatron was the hook that
lured him into Radio Shack and
then sparked his lifelong interest in
engineering: “I would roll pennies
and use them to buy soldering irons
and solder at Radio Shack.”
Burrell had a fateful reunion with
the toy while in grad school for engineering. “One of my office mates had
an Armatron at his desk,” he recalls,
“and it was broken. We took it apart
together, and that was the first time
I had seen the guts of it.
MJ25-back_armatron.indd 62
Clockwise from
top left:
The Armatron’s
inventor, Hiroyuki
Watanabe, in
Tokyo, 2025.
Watanabe at work
at Tomy in Tokyo,
1982.
A page from
the 1984 Radio
Shack catalogue,
featuring the
Armatron for
$31.95.
The Armatron on
the cover of the
November/December
1982 issue of
Robotics Age
magazine.
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COURTESY OF TAKARA TOMY (PORTRAIT); COURTESY OF HIROYUKI WATANABE (SNAPSHOT); COURTESY OF RADIOSHACKCATALOGS.COM
63
“It had this fantastic mechanical
gear train to just engage and disengage this one motor in a bunch of
different ways. And it was really fascinating that it had done so much—
the one little motor. And that sort of
got me back thinking about industrial robot arms again.”
Eric Paulos, a professor of electrical engineering and computer science at the University of California,
Berkeley, recalls nagging his parents about what an educational gift
Armatron would make. Ultimately,
he succeeded in his lobbying.
“It was just endless exploration
of picking stuff up and moving it
around and even just watching it
move. It was mesmerizing to me. I
felt like I really owned my own little robot,” he recalls. “I cherish this
thing. I still have it to this day, and
it’s still working.”
Today, Paulos builds robots and
teaches his students how to build
their own. He challenges them to
solve problems within constraints,
such as building with cardboard or
“There’s research to this
day using AI to try to
figure out optimal ways
to grab objects that
[a robot] sees in a bin or
out in the world.”
MJ25-back_armatron.indd 63
Play-Doh; he believes the restrictions facing Watanabe and his team
ultimately forced them to be more
creative in their engineering.
It’s not very hard to draw connections between the Armatron—an
impossibly analog robot—and highly
advanced machines that are today
learning to move in incredible new
ways, powered by AI advancements
like computer vision and reinforcement learning.
Paulos sees parallels between
the problems he tackled as a kid
with his Armatron and those that
researchers are still trying to deal
with today: “What happens when
you pick things up and they’re too
heavy, but you can sort of pick it
up if you approach it from different
angles? Or how do you grip things?
There’s research to this day using
AI to try to figure out optimal ways
to grab objects that [a robot] sees in
a bin or out in the world.”
While AI may be taking over
the world of robotics, the field still
requires engineers—builders and
tinkerers who can problem-solve
in the physical world.
The Armatron encouraged kids
to explore these analog mechanics, a
reminder that not all breakthroughs
happen on a computer screen. And
that hands-on curiosity hasn’t faded.
Today, a new generation of fans are
rediscovering the Armatron through
online communities and DIY modifications. Dozens of Armatron videos are on YouTube, including one
where the arm has been modified
to run on steam power.
“I’m very happy to see people
who love mechanisms are amazed,”
Watanabe told me. “I’m really happy
that there are still people out there
who love our products in this way.”
Jon Keegan writes about
technology and AI and
publishes Beautiful Public
Data, a curated collection
of government data sets
(beautifulpublicdata.com).
3/2 /25
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Fighting drug
overdoses
with science
Could NIST’s early warning system for new
adulterants in street drugs help save lives?
By Adam Bluestein
MJ25-back_NIST.indd 64
In 2021, the Maryland Department of Health and the state police
were confronting a crisis: Fatal drug overdoses in the state were
at an all-time high, and authorities didn’t know why. There was
a general sense that it had something to do with changes in the
supply of illicit drugs—and specifically of the synthetic opioid
fentanyl, which has caused overdose deaths in the US to roughly
double over the past decade, to more than 100,000 per year.
But Maryland officials were flying blind when it came to
understanding these fluctuations in anything close to real time.
The US Drug Enforcement Administration reported on the
purity of drugs recovered in enforcement operations, but the
DEA’s data offered limited detail and typically came back six to
nine months after the seizures. By then, the actual drugs on the
street had morphed many times over. Part of the investigative
challenge was that fentanyl can be some 50 times more potent
than heroin, and inhaling even a small amount can be deadly.
This made conventional methods of analysis, which required
handling the contents of drug packages directly, incredibly risky.
Seeking answers, Maryland officials turned to scientists at
the National Institute of Standards and Technology, the national
metrology institute for the United States, which defines and
maintains standards of measurement essential to a wide range
of industrial sectors and health and security applications.
There, a research chemist named Ed Sisco and his team had
developed methods for detecting trace amounts of drugs, explosives, and other dangerous materials—techniques that could
protect law enforcement officials and others who had to collect
these samples. Essentially, Sisco’s lab had fine-tuned a technology called DART (for “direct analysis in real time”) mass spectrometry—which the US Transportation Security Administration
uses to test for explosives by swiping your hand—to enable the
detection of even tiny traces of chemicals collected from an
investigation site. This meant that nobody had to open a bag or
handle unidentified powders; a usable residue sample could be
obtained by simply swiping the outside of the bag.
Sisco realized that first responders or volunteers at needle
exchange sites could use these same methods to safely collect drug
Left: Ed Sisco’s lab at
NIST developed a test that
gives law enforcement and
public health officials
vital information about
what substances are
present in street drugs.
B. HAYES/NIST (PORTRAIT); ANGELA WEISS/AFP VIA GETTY IMAGES
64
Right: A field researcher
with the New York City
Department of Health
tests a heroin sample for
xylazine.
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residue from bags, drug paraphernalia, or used test strips—which
also meant they would no longer need to wait for law enforcement to seize drugs for testing. They could then safely mail the
samples to NIST’s lab in Maryland and get results back in as little as 24 hours, thanks to innovations in Sisco’s lab that shaved
the time to generate a complete report from 10 to 30 minutes
to just one or two. This was partly enabled by algorithms that
allowed them to skip the time-consuming step of separating the
compounds in a sample before running an analysis.
The Rapid Drug Analysis and Research (RaDAR) program
launched as a pilot in October 2021 and uncovered new, critical information almost immediately. Early analysis found
xylazine—a veterinary sedative that’s been associated with
gruesome wounds in users—in about 80% of opioid samples
they collected.
This was a significant finding, Sisco says: “Forensic labs
care about things that are illegal, not things that are not illegal but do potentially cause harm. Xylazine is not a scheduled
compound, but it leads to wounds that can lead to amputation,
and it makes the other drugs more dangerous.” In addition to
the compounds that are known to appear in high concentrations in street drugs—xylazine, fentanyl, and the veterinary
sedative medetomidine—NIST’s technology can pick out
trace amounts of dozens of adulterants that swirl through the
street-drug supply and can make it more dangerous, including
acetaminophen, rat poison, and local anesthetics like lidocaine.
What’s more, the exact chemical formulation of fentanyl on
the street is always changing, and differences in molecular
structure can make the drugs deadlier. So Sisco’s team has
developed new methods for spotting these “analogues”—
compounds that resemble known chemical structures of fentanyl and related drugs.
The RaDAR program has expanded to work with partners in
public health, city and state law enforcement, forensic science,
and customs agencies at about 65 sites in 14 states. Sisco’s lab
processes 700 to 1,000 samples a month. About 85% come from
public health organizations that focus on harm reduction (an
approach to minimizing negative impacts of drug use for people
who are not ready to quit). Results are shared at these collection
points, which also collect survey data about the effects of the drugs.
Jason Bienert, a wound-care nurse at Johns Hopkins who formerly volunteered with a nonprofit harm reduction organization
in rural northern Maryland, started participating in the RaDAR
program in spring 2024. “Xylazine hit like a storm here,” he says.
“Everyone I took care of wanted to know what was in their drugs
because they wanted to know if there was xylazine in it.” When
the data started coming back, he says, “it almost became a race
to see how many samples we could collect.” Bienert sent in about
14 samples weekly and created a chart on a dry-erase board, with
drugs identified by the logos on their bags, sorted into columns
according to the compounds found in them: heroin, fentanyl,
xylazine, and everything else.
MJ25-back_NIST.indd 66
“It was a super useful tool,” Bienert says. “Everyone accepted
the validity of it.” As people came back to check on the results
of testing, he was able to build rapport and offer additional
support, including providing wound care for about 50 people a week.
The breadth and depth of testing under the RaDAR program
allow an eagle’s-eye view of the national street-drug landscape—
and insights about drug trafficking. “We’re seeing distinct fingerprints from different states,” says Sisco. NIST’s analysis
shows that fentanyl has taken over the opioid market—except
for pockets in the Southwest, there is very little heroin on the
streets anymore. But the fentanyl supply varies dramatically as
you cross the US. “If you drill down in the states,” says Sisco,
“you also see different fingerprints in different areas.” Maryland,
for example, has two distinct fentanyl supplies—one with xylazine and one without.
In summer 2024, RaDAR analysis detected something
really unusual: the sudden appearance of an industrial-grade
chemical called BTMPS, which is used to preserve plastic, in
drug samples nationwide. In the human body, BTMPS acts as
a calcium channel blocker, which lowers blood pressure, and
mixed with xylazine or medetomidine, can make overdoses
harder to treat. Exactly why and how BTMPS showed up in the
drug supply isn’t clear, but it continues to be found in fentanyl
samples at a sustained level since it was initially detected. “This
was an example of a compound we would have never thought
to look for,” says Sisco.
To Sisco, Bienert, and others working on the public health
front of the drug crisis, the ever-shifting chemical composition
of the street-drug supply speaks to the futility of the “war on
drugs.” They point out that a crackdown on heroin smuggling is
what gave rise to fentanyl. And NIST’s data shows how in June
2024—the month after Pennsylvania governor Josh Shapiro
signed a bill to make possession of xylazine illegal in his state—
it was almost entirely replaced on the East Coast by the next
veterinary drug, medetomidine.
Over the past year, for reasons that are not fully understood,
drug overdose deaths nationally have been falling for the first
time in decades. One theory is that xylazine has longer-lasting
effects than fentanyl, which means people using drugs are taking them less often. Or it could be that more and better information about the drugs themselves is helping people make
safer decisions.
“It’s difficult to say the program prevents overdoses and saves
lives,” says Sisco. “But it increases the likelihood of people coming
in to needle exchange centers and getting more linkages to wound
care, other services, other education.” Working with public health
partners “has humanized this entire area for me,” he says. “There’s
a lot more gray than you think—it’s not black and white. And it’s
a matter of life or death for some of these people.”
Adam Bluestein writes about innovation in business,
science, technology, and the creative economy.
3/2 /25
: 1 M
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68
Above and right: Tarot:
A Tale of Seven Pages is
an AI-generated web comic
series created by the South
Korean startup Onoma AI.
MJ25-back_comics.indd 68
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69
COURTESY OF THE PUBLISHER
Generative AI
is reshaping
South Korea’s
web comics industry
“My mind is still sharp and my hands work just fine, so I
have no interest in getting help from AI to draw or write
stories,” says Lee Hyun-se, a legendary South Korean
cartoonist best known for his seminal series A Daunting
Team, a 1983 manhwa about the coming-of-age of heroic
underdog baseball players. “Still, I’ve joined hands with
AI to immortalize my characters Kkachi, Umji, and Ma
Dong-tak.”
By embracing generative AI, Lee is charting a new creative frontier in South Korea’s web comics industry. Since
comics magazines faded at the turn of the century, web
comics—serialized comics that read from top to bottom
on digital platforms—have gone from niche subculture to
global entertainment powerhouse, drawing in hundreds
of millions of readers around the world. Lee has long
been at its forefront, pushing the boundaries of his craft.
Lee drew inspiration for his renegade baseball avengers from the Sammi Superstars, one of South Korea’s first
professional baseball teams, whose journey of perseverance captivated a country stifled by military dictatorship.
The series gained a cult following among readers seeking
a creative escape from political repression, mesmerized
by his bold brushstrokes and cinematic compositions
that defied the conventions of cartoons.
Kkachi, the rebellious protagonist in A Daunting Team,
is an alter ego of Lee himself. A scrappy outcast with
untamed, spiky hair, he is a fan favorite who challenges
the world with unrelenting passion and a brave conscience. He has reappeared throughout Lee’s signature
works, painted with a new layer of pathos each time—a
supernatural warrior who saves Earth from an alien attack
in Armageddon and a rogue police officer battling a powerful criminal syndicate in Karon’s Dawn. Over decades,
Kkachi has become a cultural icon in South Korea.
But Lee worries about Kkachi’s future. “In South
Korea, when an author dies, his characters also get buried
in his grave,” he says, drawing contrasts with enduring
The technology is unlocking new creative
possibilities while fueling anxieties over artistic
agency and authorship. By Michelle Kim
MJ25-back_comics.indd 69
4/2/25 1:15 PM
70
MJ25-back_comics.indd 70
to human expressions. The grand vision
of his experimental AI project is to create
a “Lee Hyun-se simulation agent”—an
advanced generation of his AI model that
replicates his creative mind. The model
would be trained on digital archives of
Lee’s essays, interviews, and texts from
his comics—the subject of an exhibit at
the National Library of Korea last year—
to encode his philosophy, personality, and
values. “It’s going to take a long time for
AI to learn my myriad worldviews because
I’ve published so much work,” he says.
The digital clone of Lee would generate new comics with his artistic intuition,
perceiving its environment and making
creative choices as he would—perhaps
even publishing a series far in the future
starring Kkachi as a post-human protagonist. “Fifty years from now, what kinds
of comics would Lee Hyun-se create if
he saw the world then?” Lee asks. “The
question fascinates me.”
L
ee’s quest for a lasting artistic legacy
is part of a broader creative evolution
driven by technology. In the decades
since their emergence, web comics have
transformed the art of storytelling, offering
an infinite digital canvas that integrates
music, animation, and interactive visuals
with the effects of new tools like automated
coloring programs. The addition of AI is
spurring the next wave of innovation. But
even as it unlocks new creative possibilities, it is fueling anxieties over artistic
agency and authorship.
Last year the South Korean startup
Onoma AI, named after the Greek word
for “name” (a signal of its ambition to
redefine creative storytelling), launched
an AI-powered web comic generator called
TooToon. The software allows users to create synopses, characters, and storyboards
with simple text prompts and convert
rough sketches into polished illustrations
that reflect their personal artistic style.
TooToon claims to streamline the laborintensive creative process by cutting down
the production time between concept
development and line art from six months
to just two weeks.
Companies like Onoma AI champion
the idea that AI can help anyone be an
artist—even if you can’t draw or afford to
hire an army of assistants to keep up with
the industry’s insane production demands.
In their vision, artists would emerge as
directors of their own AI-powered solo
studios, automating the grunt work of
drawing and channeling their creative
energy into storytelling and art direction.
The productivity breakthrough, they say,
would help artists brainstorm more experimental ideas, take on big-scale productions, and disrupt the studio monopolies
that dominate the market.
“AI would expand the web comic ecosystem,” says Song Min, the founder and
CEO of Onoma AI. Song describes the
industry in South Korea as a “pyramid”—
powerhouse platforms like Naver Webtoon
and Kakao Webtoon at the top, followed
by big-shot studios, where artists collaborate to mass-produce web comics. “The
rest of the artists, those outside the studio
system, can’t create alone,” he explains. “AI
would empower more artists to emerge as
independent artists.”
Last year, Onoma AI partnered with a
group of young web comic artists to create Tarot: A Tale of Seven Pages, a mystery
thriller unraveling the twisted fates of
strangers cursed by a hand of tarot cards.
Through these collaborations, Song uses
the artists’ feedback to refine TooToon. Still,
even as a champion of AI-generated art,
he questions whether it’s “a good thing for
AI to be perfect.” Just as engineers need to
keep coding to hone their skills, he wonders if AI should leave room for artists to
keep drawing to nurture their craft.
COURTESY OF THE PUBLISHER
American comic characters like Superman
and Spider-Man. Lee craves artistic immortality. He wants his characters to stay alive
not just in the memories of readers, but
also on their web comic platforms. “Even
after I die, I want my worldviews and characters to communicate and resonate with
the people of a new era,” he says. “That’s
the kind of immortality I want.”
Lee believes that AI can help him realize his vision. In partnership with Jaedam
Media, a web comics production company based in Seoul, he developed the
“Lee Hyun-se AI model” by fine-tuning
the open-source AI art generator Stable
Diffusion, created by the UK-based startup
Stability AI. Using a data set of 5,000 volumes of comics that he has published over
46 years, the resulting model generates
comics in his signature style.
This year, Lee is preparing to publish
his first AI-assisted web comic, a remake
of his 1994 manhwa Karon’s Dawn. Writers
at Jaedam Media are adapting the story
into a modernized crime drama starring
Kkachi as a police officer in present-day
Seoul and his love interest Umji as a daring
prosecutor. Students at Sejong University,
where Lee teaches comics, are creating
the artwork using his AI model.
The creative process unfolds in several
stages. First, Lee’s AI model generates illustrations based on text prompts and reference images, like 3D anatomy models and
hand-drawn sketches that provide cues for
different movements and gestures. Lee’s
students then curate and edit the illustrations, adjusting the characters’ poses,
tailoring their facial expressions, and integrating them into cartoonish compositions
that AI can’t engineer. After many rounds
of refinement and regeneration, Lee steps
in to orchestrate the final product, adding
his distinct artistic edge.
“Under my direction, a character might
glare with sad eyes even when they’re angry
or ferocious eyes when they’re happy,” he
says. “It’s a subversive expression, a nuance
that AI struggles to capture. Those delicate
details I need to direct myself.”
Ultimately, Lee wants to build an AI system that embodies his meticulous approach
In AI companies’ vision, artists
could automate the grunt
work of drawing and channel
their creative energy into
storytelling and art direction.
4/2/25 1:15 PM
71
Oh Hye-seong is the protagonist of
Karon’s Dawn, an AI-assisted web
comic series by the South Korean
cartoonist Lee Hyun-se, which
will be released later this year.
MJ25-back_comics.indd 71
4/2/25 1:15 PM
“AI is an inevitable tour de force, but
for now, the big hurdles lie in artists’ perception and copyright,” he says.
Onoma AI built Illustrious, the large
language model powering TooToon,
by fine-tuning Stable Diffusion on the
Danbooru2023 data set, a public image
bank of anime-style illustrations. But Stable
Diffusion, along with other popular image
generators built on the model, has come
under fire for indiscriminately scraping
images from the internet, sparking a barrage of lawsuits over copyright infringement. In turn, web comic generators are
facing intense backlash from artists who
fear that the programs are being trained
on their art without their consent.
As companies silo their training data,
artists and readers have launched a digital campaign to boycott AI-generated web
comics. In May 2023, readers bombarded
The Knight King Returns with the Gods on
Naver Webtoon with blazingly low ratings
after discovering that AI had been used to
refine portions of the artwork. The following month, artists flooded the platform with
anonymous posts protesting “AI web comics created from theft,” sharply criticizing
Naver’s contract policy requiring artists
who publish on the platform to consent to
having their works used as AI training data.
To settle the standoff, the Korea
Copyright Commission issued a set of
guidelines in December 2023, urging AI
developers to obtain permission from copyright holders before using their works as
training data; articulate the purpose, scope,
and duration of use; and provide fair compensation. A year later, amid growing calls
from AI companies for access to more data,
the South Korean government proposed
carving out an exemption to copyright laws
that would allow AI models to be trained
on copyrighted works under the doctrine
of fair use. But no legislation or regulation
has yet established a clear legal framework,
leaving artists in limbo.
Above, a strip from
A Daunting Team,
a 1983 baseball manhwa
made by Lee Hyun-se.
MJ25-back_comics.indd 72
W
hile seasoned artists like Lee
embrace the technology as a
tool to expand their legacy,
wholeheartedly licensing their intellectual
COURTESY OF THE PUBLISHER
72
4/2/25 1:15 PM
73
Creativity is “deeply
intertwined with the human
experience and its afflictions …
Can you create without a soul?
Who knows?”
property to AI, younger artists see it as a
threat. They fear that AI will steal their
artwork and, more important, their identity as artists.
“Drawing is the most difficult and the
most fun part of making comics,” says Park
So-won, a young web comic artist based in
Seoul. Park grew up dreaming of becoming
a cartoonist, watching her mother, an animator, bring characters to life. After years
of juggling gigs as an artist assistant at a
web comics studio, interrupted by a brief
creative hiatus, she made her breakthrough
on the platform Lezhin Comics with Legs
That Won’t Walk, a queer romance noir
about a boxer who falls in love with a loan
shark chasing after him over his alcoholic
father’s debt.
As an independent artist, Park is constantly at work. She publishes a new episode
every 10 days, often pulling all-nighters to
produce up to 80 cuts of drawing, even with
the help of assistants handling background
art and coloring. Occasionally she finds
herself in a flow state, working 30 hours
straight without a break.
Still, Park can’t imagine outsourcing
her drawings, which she sees as the heart
of her comics, to AI. “The crux of a comic,
however important the story, is the drawing. If the story were written in words,
people wouldn’t have read it, would they?
The story is just a thought—the execution
is the drawing,” she says. “The grammar
of comics is the drawing.” Handing over
her drawing would mean surrendering her
artistic agency.
Park thinks algorithmic art lacks soul—
like “objects that exist in a void”—and isn’t
worried about whether AI can draw better
than she does. Her drawings have evolved
over the years, shaped by her shifting
outlook on the world and breaking new
creative ground over time—an artistic
progression that she thinks an algorithm
trained to emulate existing works could
MJ25-back_comics.indd 73
never make. “I’ll keep charting new territory as an artist, while AI will stay the
same,” she says.
To Park, art is supreme indulgence:
“I’ve come this far because I love to draw.
If AI takes away my favorite thing to do in
the world, what would I do?”
But other comic artists, whose strengths
lie in storytelling, welcome the innovation.
Bae Jin-soo was an aspiring screenwriter
before debuting as an artist on Naver
Webtoon’s amateur comics page in 2010.
To turn his screenplay into a comic, Bae
taught himself to draw by photographing
different compositions and tracing them
on paper. “I can’t draw, so I’ll bet on my
writing,” he thought.
After his debut series Friday: Forbidden
Tales took off, Bae rose to stardom with
his three-part series Money Game, Pie
Game, and Funny Game—brainy psychological thrillers packed with plot twists and
witty, thought-provoking narratives about
a group of contestants playing eccentric
games to win a cash prize. They have even
inspired a popular Netflix adaptation, The
8 Show.
“I still have so many more stories I
want to tell,” Bae says. A prolific writer,
he keeps a running list of new ideas in a
pocket notepad, the genre-bending plots
spanning horror, politics, and black comedy. But with his mind racing ahead of his
hand, breathing life into all his ideas would
require commissioning a studio to execute
the illustrations. For Bae, an AI-powered
web comic generator could be a game
changer. “If AI could handle my artwork,
I would create an endless stream of new
comics,” he says.
Bae is also eager to explore AI as a
“backup battery for story ideas,” like a writer’s assistant. Even so, to hold his ground
as an artist, he plans to dig deeper into his
imagination to generate original and experimental ideas that could be found nowhere
else. “That’s the domain of [human] creators,” he says. Still, Bae wonders if his own
creative edge would slowly erode through
extensive collaboration with AI: “Would
my own colors start to fade?”
Meanwhile, comics students at Sejong
University in Seoul are learning to integrate AI into their tool kits. The budding
artists are being trained as “creative coders,” turning strips of comics into data sets
by meticulously annotating their content,
and as prompt engineers who can guide
AI to produce characters that align with
their aesthetic sensibilities.
“Creativity takes time—to reflect and
contemplate on your work,” says Han
Chang-wan, a professor of comics and animation at Sejong University, who teaches
a class on AI-generated web comics. Han
says that’s what AI will buy for his students: the time to “create more diverse
characters, more kaleidoscopic plots, and
more eclectic genres” that challenge the
formulaic comics mass-produced by studios. Ultimately, he hopes, they’ll “tap into
an entirely new readership.”
As artists navigate this uncharted
future, generative AI is raising profound
questions about what powers creativity.
“AI could be a technical assistant to artists,” says Shin Il-sook, the president of
the Korea Cartoonist Association and the
renowned cartoonist behind the historical fantasy romance The Four Daughters
of Armian, which follows a brave-hearted
princess exiled from a matriarchal kingdom
as she embarks on a journey of survival
and self-discovery through war, love, and
political power battles. Still, she wonders
if AI can really be a creative companion.
“Creativity is about making something
never seen before, driven by a desire to
share it with other people,” Shin says.
“It’s deeply intertwined with the human
experience and its afflictions. That’s why
an artist who has walked through life’s
suffering and honed their craft produces
remarkable art,” she says. “Can you create
without a soul? Who knows?”
Michelle Kim is a freelance journalist and lawyer based in Seoul.
4/2/25 1:15 PM
74
The
AI
is
present
In 2021, 20 years after the death of her
older sister, Vauhini Vara was still unable
to tell the story of her loss. “I wondered,”
she writes in Searches, her new collection of essays on AI technology, “if Sam
Altman’s machine could do it for me.” So
she tried ChatGPT. But as it expanded
on Vara’s prompts in sentences ranging
from the stilted to the unsettling to the
sublime, the thing she’d enlisted as a tool
stopped seeming so mechanical.
“Once upon a time, she taught me to
exist,” the AI model wrote of the young
woman Vara had idolized. Vara, a journalist and novelist, called the resulting
essay “Ghosts,” and in her opinion, the
best lines didn’t come from her: “I found
myself irresistibly attracted to GPT-3—to
the way it offered, without judgment, to
deliver words to a writer who has found
MJ25-back_books.indd 74
herself at a loss for them … as I tried to
write more honestly, the AI seemed to be
doing the same.”
The rapid proliferation of AI in our
lives introduces new challenges around
authorship, authenticity, and ethics in
work and art. But it also offers a particularly human problem in narrative: How
can we make sense of these machines, not
just use them? And how do the words we
choose and stories we tell about technology affect the role we allow it to take on
(or even take over) in our creative lives?
Both Vara’s book and The Uncanny Muse,
a collection of essays on the history of
art and automation by the music critic
David Hajdu, explore how humans have
historically and personally wrestled with
the ways in which machines relate to our
own bodies, brains, and creativity. At the
Three books examine what
we gain and lose when we let
machines create.
By Rebecca Ackermann
Illustration by Shout
Searches:
Selfhood in the Digital Age
Vauhini Vara
PANTHEON, 2025
The Uncanny Muse:
Music, Art, and Machines
from Automata to AI
David Hajdu
W.W. NORTON & COMPANY, 2025
The Mind Electric:
A Neurologist on the Strangeness
and Wonder of Our Brains
Pria Anand
WASHINGTON SQUARE PRESS, 2025
3/2 /25
: 4 M
MJ25-back_books.indd 75
3/2 /25
: 4 M
76
same time, The Mind Electric, a new book
by a neurologist, Pria Anand, reminds us
that our own inner workings may not be
so easy to replicate.
Searches is a strange artifact. Part
memoir, part critical analysis, and part
AI-assisted creative experimentation,
Vara’s essays trace her time as a tech
reporter and then novelist in the San
Francisco Bay Area alongside the history of the industry she watched grow up.
Tech was always close enough to touch:
One college friend was an early Google
employee, and when Vara started reporting on Facebook (now Meta), she and
Mark Zuckerberg became “friends” on
his platform. In 2007, she published a
scoop that the company was planning to
introduce ad targeting based on users’ personal information—the first shot fired in
the long, gnarly data war to come. In her
essay “Stealing Great Ideas,” she talks about
turning down a job reporting on Apple to
go to graduate school for fiction. There, she
wrote a novel about a tech founder, which
was later published as The Immortal King
Rao. Vara points out that in some ways at
the time, her art was “inextricable from
the resources [she] used to create it”—
products like Google Docs, a MacBook, an
iPhone. But these pre-AI resources were
tools, plain and simple. What came next
was different.
Interspersed with Vara’s essays are
chapters of back-and-forths between
the author and ChatGPT about the book
itself, where the bot serves as editor at
Vara’s prompting. ChatGPT obligingly
summarizes and critiques her writing in
a corporate-shaded tone that’s now familiar to any knowledge worker. “If there’s a
place for disagreement,” it offers about the
first few chapters on tech companies, “it
might be in the balance of these narratives.
Some might argue that the benefits—such
as job creation, innovation in various sectors like AI and logistics, and contributions
to the global economy—can outweigh the
negatives.”
Vara notices that ChatGPT writes “we”
and “our” in these responses, pulling it
into the human story, not the tech one:
MJ25-back_books.indd 76
“Earlier you mentioned ‘our access to
information’ and ‘our collective experiences and understandings.’” When
she asks what the rhetorical purpose of
that choice is, ChatGPT responds with
a numbered list of benefits including
“inclusivity and solidarity” and “neutrality and objectivity.” It adds that “using
the first-person plural helps to frame
the discussion in terms of shared human
experiences and collective challenges.”
Does the bot believe it’s human? Or at
least, do the humans who made it want
other humans to believe it does? “Can
corporations use these [rhetorical] tools
in their products too, to subtly make people identify with, and not in opposition
to, them?” Vara asks. ChatGPT replies,
“Absolutely.”
curtain. The GPT models and others are
trained through human labor, in sometimes exploitative conditions. And much
of the training data was the creative work
of human writers before her. “I’d conjured
artificial language about grief through
the extraction of real human beings’ language about grief,” she writes. The creative
ghosts in the model were made of code,
yes, but also, ultimately, made of people.
Maybe Vara’s essay helped cover up that
truth too.
In the book’s final essay, Vara offers a
mirror image of those AI call-and-response
exchanges as an antidote. After sending out
an anonymous survey to women of various ages, she presents the replies to each
question, one after the other. “Describe
something that doesn’t exist,” she prompts,
The rapid proliferation of AI in our lives
introduces new challenges around
authorship, authenticity, and ethics in
work and art. How can we make sense
of these machines, not just use them?
Vara has concerns about the words
she’s used as well. In “Thank You for Your
Important Work,” she worries about the
impact of “Ghosts,” which went viral after
it was first published. Had her writing
helped corporations hide the reality of
AI behind a velvet curtain? She’d meant
to offer a nuanced “provocation,” exploring how uncanny generative AI can be.
But instead, she’d produced something
beautiful enough to resonate as an ad for
its creative potential. Even Vara herself felt
fooled. She particularly loved one passage
the bot wrote, about Vara and her sister as
kids holding hands on a long drive. But she
couldn’t imagine either of them being so
sentimental. What Vara had elicited from
the machine, she realized, was “wish fulfillment,” not a haunting.
The machine wasn’t the only thing
crouching behind that too-good-to-be-true
and the women respond: “God.” “God.”
“God.” “Perfection.” “My job. (Lost it.)”
Real people contradict each other, joke,
yell, mourn, and reminisce. Instead of a
single authoritative voice—an editor, or
a company’s limited style guide—Vara
gives us the full gasping crowd of human
creativity. “What’s it like to be alive?” Vara
asks the group. “It depends,” one woman
answers.
David Hajdu, now music editor at The
Nation, and previously a music critic for
The New Republic, goes back much further than the early years of Facebook to
tell the history of how humans have made
and used machines to express ourselves.
Player pianos, microphones, synthesizers, and electrical instruments were all
assistive technologies that faced skepticism before acceptance and, sometimes,
elevation in music and popular culture.
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78
They even influenced the kind of art
people were able to and wanted to make.
Electrical amplification, for instance,
allowed singers to use a wider vocal range
and still reach an audience. The synthesizer introduced a new lexicon of sound
to rock music. “What’s so bad about being
mechanical, anyway?” Hajdu asks in The
Uncanny Muse. And “what’s so great about
being human?”
But Hajdu is also interested in how
intertwined the history of man and
machine can be, and how often we’ve
used one as a metaphor for the other.
Descartes saw the body as empty machinery for consciousness, he reminds us.
Hobbes wrote that “life is but a motion
of limbs.” Freud described the mind
as a steam engine. Andy Warhol told
an interviewer that “everybody should
be a machine.” And when computers
entered the scene, humans used them as
metaphors for themselves too. “Where
the machine model had once helped us
understand the human body … a new
category of machines led us to imagine
the brain (how we think, what we know,
even how we feel or how we think about
what we feel) in terms of the computer,”
Hajdu writes.
But what is lost with these one-toone mappings? What happens when we
imagine that the complexity of the brain—
an organ we do not even come close to
fully understanding—can be replicated
in 1s and 0s? Maybe what happens is we
get a world full of chatbots and agents,
computer-generated artworks and AI DJs,
that companies claim are singular creative
voices rather than remixes of a million
human inputs. And perhaps we also get
projects like the painfully named Painting
Fool—an AI that paints, developed by
Simon Colton, a scholar at Queen Mary
University of London. He told Hajdu that
he wanted to “demonstrate the potential of
a computer program to be taken seriously
as a creative artist in its own right.” What
Colton means is not just a machine that
makes art but one that expresses its own
worldview: “Art that communicates what
it’s like to be a machine.”
MJ25-back_books.indd 78
Hajdu seems to be curious and
optimistic about this line of inquiry.
“Machines of many kinds have been
communicating things for ages, playing
invaluable roles in our communication
through art,” he says. “Growing in intelligence, machines may still have more
to communicate, if we let them.” But the
question that The Uncanny Muse raises
at the end is: Why should we art-making
humans be so quick to hand over the
paint to the paintbrush? Why do we care
how the paintbrush sees the world? Are
we truly finished telling our own stories
ourselves?
Pria Anand might say no. In The Mind
Electric, she writes: “Narrative is universally, spectacularly human; it is as
unconscious as breathing, as essential
along seams that few can find, and—
yes—see and hear ghosts. In fact, Anand
cites one study of 375 college students
in which researchers found that nearly
three-quarters “had heard a voice that
no one else could hear.” These were not
diagnosed schizophrenics or sufferers of
brain tumors—just people listening to
their own uncanny muses. Many heard
their name, others heard God, and some
could make out the voice of a loved one
who’d passed on. Anand suggests that
writers throughout history have harnessed organic exchanges with these
internal apparitions to make art. “I see
myself taking the breath of these voices
in my sails,” Virginia Woolf wrote of her
own experiences with ghostly sounds. “I
am a porous vessel afloat on sensation.”
What happens when we imagine that
the complexity of the brain—an organ
we do not even come close to fully
understanding—can be replicated
in 1s and 0s?
as sleep, as comforting as familiarity. It
has the capacity to bind us, but also to
other, to lay bare, but also obscure.” The
electricity in The Mind Electric belongs
entirely to the human brain—no metaphor
necessary. Instead, the book explores a
number of neurological afflictions and
the stories patients and doctors tell to
better understand them. “The truth of our
bodies and minds is as strange as fiction,”
Anand writes—and the language she uses
throughout the book is as evocative as
that in any novel.
In personal and deeply researched
vignettes in the tradition of Oliver
Sacks, Anand shows that any comparison between brains and machines will
inevitably fall flat. She tells of patients
who see clear images when they’re functionally blind, invent entire backstories when they’ve lost a memory, break
The mind in The Mind Electric is vast,
mysterious, and populated. The narratives people construct to traverse it are
just as full of wonder.
Humans are not going to stop using
technology to help us create anytime
soon—and there’s no reason we should.
Machines make for wonderful tools, as
they always have. But when we turn the
tools themselves into artists and storytellers, brains and bodies, magicians and
ghosts, we bypass truth for wish fulfillment. Maybe what’s worse, we rob ourselves of the opportunity to contribute our
own voices to the lively and loud chorus
of human experience. And we keep others from the human pleasure of hearing
them too.
Rebecca Ackermann is a writer,
designer, and artist based in San
Francisco.
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DISCO
VE
RI
E
S
AN
N
XA
NDRA CHAN
G
S
MJ25-back_fiction.indd 80
RE
BY
LE
D FA I
L
U
FICTIO
A
4/1/25 12:3 PM
Fiction
81
Sophie and Martin are at the 2012 Gordon Research
Conference on the Biology of Aging in Ventura, California.
It is a foggy February weekend. Both are disappointed about how
little sun there is on the California beach.
They are two graduate students—Sophie in her sixth and
final year, Martin in his fourth—who have traveled from different East Coast cities to present posters on their work. Martin’s
shows health data collected from supercentenarians compared
with the general Medicare population, capturing the diseases
that are less and more common in the populations. Sophie is
presenting on her recently accepted first-author paper in Aging
Cell on two specific genes that, when activated, extend lifespan
in C. elegans roundworms, the model organism of her research.
1
Sophie walks by Martin’s poster after she is done presenting her own. She is not immediately impressed by
his work. It is not published, for one thing. But she sees how it
is attention-grabbing and relevant, even necessary. He has a
little crowd listening to him. He notices her—a frowning girl—
standing in the back and begins to talk louder, hoping she hears.
“Supercentenarians are much less likely to have seven diseases,” he says, pointing to his poster. “Alzheimer’s, heart failure,
diabetes, depression, prostate cancer, hip fracture, and chronic
kidney disease. Though they have higher instances of four diseases, which are arthritis, cataracts, osteoporosis, and glaucoma.
These aren’t linked to mortality, but they do affect quality of life.”
What stands out to Sophie is the confidence in Martin’s voice,
despite the unsurprising nature of the findings. She admires that
sound, its sturdiness. She makes note of his name and plans to
seek him out.
2
They find one another in the hotel bar among other graduate students. The students are talking about the logistics
of their futures: Who is going for a postdoc, who will opt for
industry, do any have job offers already, where will their research
have the most impact, is it worth spending years working toward
something so uncertain? They stay up too late, dissecting journal
articles they’ve read as if they were debating politics. They enjoy
the freedom away from their labs and PIs.
Martin says, again with that confidence, that he will become
a professor. Sophie says she likely won’t go down that path. She
has received an offer to start as a scientist at an aging research
startup called Abyssinian Bio, after she defends. Martin says,
“Wouldn’t your work make more sense in an academic setting,
where you have more freedom and power over what you do?”
She says, “But that could be years from now and I want to start
my real life, so …”
US FOOD & DRUG ADMINISTRATION
3
MJ25-back_fiction.indd 81
Martin is enamored with Sophie. She is not only brilliant;
she is helpful. She strengthens his papers with precise
edits and grounds his arguments with stronger evidence. Sophie
is enamored with Martin. He is not only ambitious; he is
4-18
4/1/25 12:3 PM
82
Fiction
supportive and adventurous. He encourages her to try new activities and tools, both in and out of work, like learning to ride a
motorcycle or using CRISPR.
Martin visits Sophie in San Francisco whenever he can,
which amounts to a weekend or two every other month. After
two years, their long-distance relationship is taking its toll. They
want more weekends, more months, more everything together.
They make plans for him to get a postdoc near her, but after
multiple rejections from the labs where he most wants to work,
his resentment toward academia grows.
“They don’t see the value of my work,” he says.
“Join Abyssinian,” Sophie offers.
The company is growing. They want more researchers
with data science backgrounds. He takes the job, drawn more
by their future together than by the science.
taking the task of living longer seriously enough. He does not
want her to die. He does not want to die.
Nobody at Abyssinian is taking the task of living longer
seriously enough. Of all the aging bio startups he could have
ended up at, how has he ended up at one with such modest—
no, lazy—goals? He begins publicly dismissing basic research
as “too slow” and “too limited,” which offends many of his and
Sophie’s colleagues.
Sophie defends him, says he is still doing good work, despite
the evidence. She is busy, traveling often for conferences, and
mistakenly misclassifies the changes in Martin’s attitude as
temporary outliers.
19
20-35 For a long time, they are happy. They marry. They do
their research. They travel. Sophie visits Martin’s extended
family in France. Martin goes with Sophie to her cousin’s wedding in Taipei. They get a dog. The dog dies. They are both devastated but increasingly motivated to better understand the
mechanisms of aging. Maybe their next dog will have the opportunity to live longer. They do not get a next dog.
Sophie moves up at Abyssinian. Despite being in industry,
her work is published in well-respected journals. She collaborates well with her colleagues. Eventually, she is promoted to
executive director of research.
Martin stalls at the rank of principal scientist, and though
Sophie is technically his boss—or his boss’s boss—he genuinely
doesn’t mind when others call him “Dr. Sophie Xie’s husband.”
At dinner on his 35th birthday, a friend jokes that Martin
is now middle-aged. Sophie laughs and agrees, though
she is older than Martin. Martin joins in the laughter, but this
small comment unlocks a sense of urgency inside him. What
once felt hypothetical—his own death, the death of his wife—
now appears very close. He can feel his wrinkles forming.
First come the subtle shifts in how he talks about his research
and Abyssinian’s work. He wants to “defeat” and “obliterate” aging,
which he comes to describe as humankind’s “greatest adversary.”
One day, during a meeting, Martin says to Jerry, a wellrespected scientist at Abyssinian and in the electron
microscopy imaging community at large, that EM is an outdated,
old, crusty technology. Martin says it is stupid to use it when
there are more advanced, cutting-edge methods, like cryo-EM
and super-resolution microscopy. Martin has always been outspoken, but this instance veers into rudeness.
At home, Martin and Sophie argue. Initially, they argue about
whether tools of the past can be useful to their work. Then the
argument morphs. What is the true purpose of their research?
Martin says it’s called anti-aging research for a reason: It’s to
defy aging! Sophie says she’s never called her work anti-aging
research; she calls it aging research or research into the biology
of aging. And Abyssinian’s overarching mission is more simply
to find druggable targets for chronic and age-related diseases.
Occasionally, the company’s marketing arm will push out messaging about extending the human lifespan by 20 years, but that has
nothing to do with scientists like them in R&D. Martin seethes.
Only 20 years! What about hundreds? Thousands?
44
40
He begins taking supplements touted by tech influencers. He goes on a calorie-restricted diet. He gets weekly
vitamin IV sessions. He looks into blood transfusions from young
donors, but Sophie tells him to stop with all the fake science.
She says he’s being ridiculous, that what he’s doing could be
dangerous.
Martin, for the first time, sees Sophie differently. Not without love, but love burdened by an opposing weight, what others might recognize as resentment. Sophie is dedicated to the
demands of her growing department. Martin thinks she is not
43
MJ25-back_fiction.indd 82
45-49 They continue to argue and the arguments are round-
about, typically ending with Sophie crying, absconding
to her sister’s house, and the two of them not speaking for short
periods of time.
What hurts Sophie most is Martin’s persistent dismissal
of death as merely an engineering problem to be solved.
Sophie thinks of the ways the C. elegans she observes regulate
their lifespans in response to environmental stress. The complex
dance of genes and proteins that orchestrates their aging process. In the previous month’s experiment, a seemingly simple
mutation produced unexpected effects across three generations
of worms. Nature’s complexity still humbles her daily. There is
still so much unknown.
50
Martin blames the past. He realizes he should have tried
harder to become a professor. Let Sophie make the industry money—he could have had academic clout. Professor Warwick.
It would have had a nice sound to it. To his dismay, everyone in
50
4/1/25 12:3 PM
Fiction
83
his lab calls him Martin. Abyssinian has a first-name policy.
Something about flat hierarchies making for better collaboration.
Good ideas could come from anyone, even a lowly, unintelligent
senior associate scientist in Martin’s lab who barely understands
how to process a data set. A great idea could come from anyone
at all—except him, apparently. Sophie has made that clear.
They live in a tenuous peace for some time, perfecting
the art of careful scheduling: separate coffee times, meetings avoided, short conversations that stick to the day-to-day
facts of their lives.
51-59
Sophie thinks
of the ways the
C. elegans she
observes regulate
their lifespans
in response to
environmental
stress. The
complex dance
of genes and
proteins that
orchestrates their
aging process.
Then Martin stands up to interrupt a presentation by
the VP of research to announce that studying natural
aging is pointless since they will soon eliminate it entirely. While
Jerry may have shrugged off Martin’s aggressiveness, the VP
does not. This leads to a blowout fight between Martin and many
of his colleagues, in which Martin refuses to apologize and calls
them all shortsighted idiots.
Sophie watches with a mixture of fear and awe. Martin thinks:
Can’t she, my wife, just side with me this once?
60
Back at home:
Martin at the kitchen counter, methodically crushing his evening supplements into powder. “I’m trying to save
humanity.” He taps the powder into his protein shake with the
precision of a scientist measuring reagents. “And all you want
to do is sit in the lab to watch worms die.”
Sophie observes his familiar movements, now foreign in their
desperation. The kitchen light catches the silver spreading at
his temples and on his chin—the very evidence of aging he is
trying so hard to erase.
“That’s not true,” she says.
Martin gulps down his shake.
“What about us? What about children?”
Martin coughs, then laughs, a sound that makes Sophie flinch.
“Why would we have children now? You certainly don’t have the
time. But if we solve aging, which I believe we can, we’d have
all the time in the world.”
“We used to talk about starting a family.”
“Any children we have should be born into a world where we
already know they never have to die.”
“We could both make the time. I want to grow old together—”
All Martin hears are promises that lead to nothing, nowhere.
“You want us to deteriorate? To watch each other decay?”
“I want a real life.”
“So you’re choosing death. You’re choosing limitation.
Mediocrity.”
61
Martin doesn’t hear from his wife for four days, despite
texting her 16 times—12 too many, by his count. He finally
breaks down enough to call her in the evening, after a couple of
64
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Fiction
glasses of aged whisky (a gift from a former colleague, which
Martin has rarely touched and kept hidden in the far back of a
desk drawer).
Voicemail. And after this morning’s text, still no glimmering
ellipsis bubble to indicate Sophie’s typing.
Forget her, he thinks, leaning back in his Steelcase chair,
adjusted specifically for his long runner’s legs and shorterthan-average torso. At 39, Martin’s spreadsheets of vitals now
show an upward trajectory; proof of his ability to reverse his
biological age. Sophie does not appreciate this. He stares out his
office window, down at the employees crawling around Abyssinian
Bio’s main quad. How small, he thinks. How significantly unaware
of the future’s true possibilities. Sophie is like them.
66
Forget her, he thinks again as he turns down a bay toward
Robert, one of his struggling postdocs, who is sitting at
his bench staring at his laptop. As Martin approaches, Robert
minimizes several windows, leaving only his home screen
behind.
“Where are you at with the NAD+ data?” Martin asks.
Robert shifts in his chair to face Martin. The skin of his neck
grows red and splotchy. Martin stares at it in disgust.
“Well?” he asks again.
“Oh, I was told not to work on that anymore?” The boy has
a tendency to speak in the lilt of questions.
“By who?” Martin demands.
“Uh, Sophie?”
“I see. Well, I expect new data by end of day.”
“Oh, but—”
Martin narrows his eyes. The red splotches on Robert’s neck
grow larger.
“Um, okay,” the boy says, returning his focus to the computer.
Martin decides a response is called for …
67
Immortality Promise
I am immortal. This doesn’t make me special. In
fact, most people on Earth are immortal. I am 6,000 years old.
Now, 6,000 years of existence give one a certain perspective. I
remember back when genetic engineering and knowledge about
the processes behind aging were still in their infancy. Oh, how
people argued and protested.
“It’s unethical!”
“We’ll kill the Earth if there’s no death!”
“Immortal people won’t be motivated to do anything! We’ll
become a useless civilization living under our AI overlords!”
I believed back then, and now I know. Their concerns had
no ground to stand on.
Eternal life isn’t even remarkable anymore, but being among
its architects and early believers still garners respect from the
world. The elegance of my team’s solution continues to fill me
with pride. We didn’t just halt aging; we mastered it. My cellular
70
MJ25-back_fiction.indd 84
machinery hums with an efficiency that would make evolution
herself jealous.
Those early protesters—bless their mortal, no-longerbeating hearts—never grasped the biological imperative of
what we were doing. Nature had already created functionally
immortal organisms—the hydra, certain jellyfish species, even
some plants. We simply perfected what evolution had sketched
out. The supposed ethical concerns melted away once people
understood that we weren’t defying nature. We were fulfilling its potential.
Today, those who did not want to be immortal aren’t around.
Simple as that. Those who are here do care about the planet more
than ever! There are almost no diseases, and we’re all very productive people. Young adults—or should I say young-looking
adults—are naturally restless and energetic. And with all this
life, you have the added benefit of not wasting your time on a
career you might hate! You get to try different things and find
out what you’re really good at and where you’re appreciated!
Life is not short! Resources are plentiful!
Of course, biological immortality doesn’t equal invincibility.
People still die. Just not very often. My colleagues in materials
science developed our modern protective exoskeletons. They’re
elegant solutions, though I prefer to rely on my enhanced reflexes
and reinforced skeletal structure most days.
The population concerns proved mathematically unfounded.
Stable reproduction rates emerged naturally once people realized they had unlimited time to start families. I’ve had four sets
of children across 6,000 years, each born when I felt truly ready
to pass on another iteration of my accumulated knowledge. With
more life, people have much more patience.
Now we are on to bigger and more ambitious projects. We
conquered survival of individuals. The next step: survival of
our species in this universe. The sun’s eventual death poses an
interesting challenge, but nothing we can’t handle. We have
colonized five planets and two moons in our solar system, and
we will colonize more. Humanity will adapt to whatever environment we encounter. That’s what we do.
My ancient motorcycle remains my favorite indulgence. I
love taking it for long cruises on the old Earth roads that remain
intact. The neural interface is state-of-the-art, of course. But
mostly I keep it because it reminds me of earlier times, when
we thought death was inevitable and life was limited to a single planet. The future stretches out before us like an infinity I
helped create—yet another masterpiece in the eternal gallery
of human evolution.
Martin feels better after writing it out. He rereads it a
couple times, feels even better. Then he has the idea to
send his writing to the department administrator. He asks her
to create a new tab on his lab page, titled “Immortality Promise,”
and to post his piece there. That will get his message across to
Sophie and everyone at Abyssinian.
71
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/5/24 11: 3 M
86
Fiction
Sophie’s boss, Ray, is the first to email her. The subject
line: “martn” [sic]. No further words in the body. Ray is
known to be short and blunt in all his communications, but his
meaning is always clear. They’ve had enough conversations about
Martin by then. She is already in the process of slowly shutting
down his projects, has been ignoring his texts and calls because
of this. Now she has to move even faster.
72
Sophie leaves her office and goes into the lab. As an
executive, she is not expected to do experiments, but
watching a thousand tiny worms crawl across their agar plates
soothes her. Each of the ones she now looks at carries a fluorescent marker she designed to track mitochondrial dynamics during
aging. The green glow pulses with their movements, like stars
blinking in a microscopic galaxy. She spent years developing
this strain of C. elegans, carefully selecting for longevity without
sacrificing health. The worms that lived longest weren’t always
the healthiest—a truth about aging that seemed to elude Martin.
Those worms taught her more about the genuine complexity of
aging. Just last week, she observed something unexpected: The
mitochondrial networks in her long-lived strains showed subtle
patterns of reorganization never documented before. The discovery felt intimate, like being trusted with a secret.
“How are things looking?” Jerry appears beside her. “That
new strain expressing the dual markers?”
Sophie nods, adjusting the focus. “Look at this network pattern. It’s different from anything in the literature.” She shifts
aside so Jerry can see. This is what she loves about science: the
genuine puzzles, the patient observation, the slow accumulation
of knowledge that, while far removed from a specific application,
could someday help people age with dignity.
“Beautiful,” Jerry murmurs. He straightens. “I heard about
Martin’s … post.”
Sophie closes her eyes for a moment, the image of the mitochondrial networks still floating in her vision. She’s read Martin’s
“Immortality Promise” piece three times, each more painful
than the last. Not because of its grandiose claims—those were
comically disconnected from reality—but because of what it’s
revealed about her husband. The writing pulsed with a frightening certainty, a complete absence of doubt or wonder. Gone was
the scientist who once spent many lively evenings debating with
her about the evolutionary purpose of aging, who delighted in
being proved wrong because it meant learning something new.
73
She sees in his words a man who has abandoned the fundamental principles of science. His piece reads like a religious text or science fiction story, casting himself as the hero. He
isn’t pursuing research anymore. He hasn’t been for a long time.
She wonders how and when he arrived there. The change in
Martin didn’t take place overnight. It was gradual, almost imperceptible—not unlike watching someone age. It wasn’t easy to
notice if you saw the person every day; Sophie feels guilty for
74
MJ25-back_fiction.indd 86
not noticing. Then again, she read a new study out a few months
ago from Stanford researchers that found people do not age linearly but in spurts—specifically, around 44 and 60. Shifts in the
body lead to sudden accelerations of change. If she’s honest with
herself, she knew this was happening to Martin, to their relationship. But she chose to ignore it, give other problems precedence.
Now it is too late. Maybe if she’d addressed the conditions right
before the spike—but how? wasn’t it inevitable?—he would not
have gone from scientist to fanatic.
“You’re giving the keynote at next month’s Gordon conference,” Jerry reminds her, pulling her back to reality.
“Don’t let this overshadow that.”
She manages a small smile. Her work has always been methodical, built on careful observation and respect for the fundamental
mysteries of biology. The keynote speech represents more than
five years of research: countless hours of guiding her teams, of
exciting discussions among her peers, of watching worms age
and die, of documenting every detail of their cellular changes. It
is one of the biggest honors of her career. There is poetry in it,
she thinks—in the collisions between discoveries and failures.
75
The knock on her office door comes at 2:45. Linda from
HR, right on schedule. Sophie walks with her to conference
room B2, two floors below, where Martin’s group resides. Through
the glass walls of each lab, they see scientists working at their
benches. One adjusts a microscope’s focus. Another pipettes clear
liquid into rows of tubes. Three researchers point at data on a screen.
Each person is investigating some aspect of aging, one careful
experiment at a time. The work will continue, with or without Martin.
In the conference room, Sophie opens her laptop and pulls
up the folder of evidence. She has been collecting it for months.
Martin’s emails to colleagues, complaints from collaborators and
direct reports, and finally, his “Immortality Promise” piece. The
documentation is thorough, organized chronologically. She has
labeled each file with dates and brief descriptions, as she would
for any other data.
76
Martin walks in at 3:00. Linda from HR shifts in her chair.
Sophie is the one to hand the papers over to Martin; this
much she owes him. They contain words like “termination” and
“effective immediately.” Martin’s face complicates itself when he
looks them over. Sophie hands over a pen and he signs quickly.
He stands, adjusts his shirt cuffs, and walks to the door. He
turns back.
“I’ll prove you wrong,” he says, looking at Sophie. But what
stands out to her is the crack in his voice on the last word.
Sophie watches him leave. She picks up the signed papers
and hands them to Linda, and then walks out herself.
77
Alexandra Chang is the author of Days of Distraction and
Tomb Sweeping and is a National Book Foundation 5 under
35 honoree. She lives in Camarillo, California.
4/1/25 12:3 PM
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Untitled-4 1
3/2 /25 12:22 PM
China’s
disillusioned
youth look to
DeepSeek to tell
them what the
future holds.
Major buzz
surrounds China’s
Manus, allegedly
the world’s first
general AI agent.
The tiny village
where DeepSeek
founder
Liang Wenfeng
grew up has
become a major
tourist destination
in China.
Robotruck startup
Waabi says its
simulations are now
accurate enough
to prove the safety
of its driverless
big rigs without
extensive testing on
real roads.
A new Google
AI is designed to
help scientists
research
complex topics,
but experts aren’t
convinced that
it’s trustworthy
enough to use.
DOOM
Donald Trump shared
an AI-generated
video reimagining
Gaza as a beach
resort, complete with
voluptuous, bearded
belly dancers.
Good news—
Stanford
researchers have
developed
new benchmarks
to help reduce
bias in AI models,
potentially making
them fairer and
less likely to
cause harm.
OpenAI calls
for a US ban on
models from the
People’s Republic
of China—
including those
made by archrival
DeepSeek.
Google’s new
model Gemini
Robotics could
make robots
more useful by
getting them to
respond to verbal
commands.
Novelists,
beware: OpenAI
claims to have
created a new
model with
a flair for
creative writing.
Elon Musk’s
DOGE claims
that its workautomating
chatbot
eliminates
the need for
many federal
employees.
The days of
obsessing over
manually writing and
debugging are over;
it’s time to start
“vibe coding”
instead.
Checkmate!
The more
sophisticated
an AI reasoning
model, the more
likely it is to
cheat at chess.
AI companion
site Botify is
hosting
underage
celebrity
chatbots
that engage in
sexually charged
conversations.
Israel has
created a
powerful
AI spying tool
trained on
intercepted
Palestinian data.
HYPE
REALITY
The AI Hype Index
Separating AI reality from
hyped-up fiction isn’t always easy.
That’s why we’ve created the
AI Hype Index—a simple, at-aglance summary of everything
you need to know about the state
of the industry.
While AI models are certainly capable
of creating interesting and sometimes
entertaining material, their output isn’t
necessarily useful. Google DeepMind is
hoping that its new robotics model could
make machines more receptive to verbal
commands, paving the way for us to simply
speak orders to them aloud. Elsewhere,
the Chinese startup Monica has created
Manus, which it claims is the very first
general AI agent to complete truly useful
tasks. And burnt-out coders are allowing
AI to take the wheel entirely in a new
practice dubbed “vibe coding.”
MIT Technology Review (ISSN 1099-274X), May/June 2025 issue, Reg. US Patent Office, is published bimonthly by MIT Technology Review, 196 Broadway, 3rd Floor, Cambridge, MA 02139. Entire contents ©2025. The editors seek diverse views, and authors’ opinions do not represent the official policies of their institutions or those of MIT. Periodicals postage paid at Boston, MA, and additional mailing offices. Postmaster: Send address
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MJ25-back_index.indd 88
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3/31/25 3:24 PM
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