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“In the latest example, the Energy Department will convert a shuttered Cold War-era uranium enrichment facility into a data center campus and gas plants.
President Trump and Energy Secretary Chris Wright during a White House event last week in which 200 companies vowed to prevent A.I. data centers from driving up power costs.Kenny Holston/The New York Times
The Trump administration is repurposing large chunks of federal land to host enormous data centers and the power plants needed to run them as officials seek to accelerate the development of artificial intelligence and overcome growing local opposition to the facilities.
The Energy Department announced on Wednesday that it would redevelop parts of a retired Cold War-era uranium enrichment facility in Paducah, Ky., to host a large new A.I. data center campus. The site is owned by the federal government and has been undergoing environmental cleanup for years.
The $100 billion project would include construction of 2 gigawatts of natural gas power connected to the local grid and 2.6 gigawatts of battery storage capacity. The Energy Department said the project would be privately funded through a partnership with NextEra Energy, one of the nation’s largest power companies; the investment firm Brookfield; and several local utilities. (One gigawatt of gas can power roughly 750,000 homes.)
“The U.S. government is leveraging its assets — like our federal lands — to add power generation, create jobs, and ensure the United States wins the A.I. race,” Energy Secretary Chris Wright said in a statement.
It’s the third time that the Trump administration has detailed plans to redevelop Cold War-era facilities into data centers. In March, the Energy Department announced that it would repurpose parts of a former nuclear enrichment facility in Piketon, Ohio, to host an enormous 10-gigawatt data center, mostly powered by natural gas and financed partly by Japan.
And this month, the Energy Department’s National Nuclear Security Administration said it would work with the engineering firm Amentum to explore a 1-gigawatt data center, potentially powered by gas and nuclear power, in Savannah River, S.C., at a federal complex once devoted to making plutonium for nuclear weapons.
President Trump has been a major supporter of artificial intelligence, which he has called essential to winning the technology race against China. In recent months, he has also tried to confront growing opposition in many communities to the proliferation of giant, energy-hungry data centers, which are driving up electricity demand.
The Paducah, Ky., plant had produced low-enriched uranium for both military reactors and nuclear weapons since the 1950s. It closed in 2013.Cavan Images, via Alamy
A recent Gallup poll found that nearly half of Americans would “strongly oppose” a new data center in their neighborhood, with many citing concerns about noise, pollution and energy and water usage. In the first quarter of this year, at least 75 data center projects worth $130 billion were delayed or stopped by local opposition, according to Data Center Watch, a project by the A.I. research firm 10a Labs.
At a White House event last week, Mr. Trump aimed to address some of those concerns by announcing that nearly 200 companies — including tech giants, utilities and data center developers — had signed a voluntary pledge to prevent A.I. data centers from increasing electricity costs for Americans. Experts said that pledge could be hard to honor in practice, since power prices are often determined by local regulators.
Mr. Trump highlighted examples where new data center projects had helped reduce local property taxes or electricity rates. And he railed against data center opponents, calling them “radical left communists” who “would not lower your electricity bills.”
His administration has moved to encourage data center development on lands owned by the Energy Department, which could offer a smoother path for approval. The Environmental Protection Agency has also taken several steps to make data centers easier to build, such as loosening limits on smog-causing pollution from the gas turbines that often power the facilities.
Separately, the Interior Department recently approved plans for the first-ever data center on federal land near Boulder City, Nev., on a site where a large utility-scale solar array had previously been permitted. The agency said it did not to conduct a new, time-consuming environmental review for the project because the data center would have impacts similar to the original solar facility. The approval was originally reported by Heatmap, a climate news site.
“By cutting the public out of the process, they’re trying to hand over Nevada’s desert to billion-dollar tech companies without anyone getting a say,” said Olivia Tanager, executive director of the Sierra Club’s chapter in the Toiyabe area of Nevada.
In Kentucky, the new data center will sit on portions of a 3,556-acre Energy Department site that produced low-enriched uranium for both military reactors and nuclear weapons since the 1950s. The enrichment facility, which used a now-obsolete gaseous diffusion method, closed in 2013.
The Energy Department said the project’s new gas-fired power plants would produce more electricity than the data center needed and could provide the surplus to local communities. The project is expected to be completed in 2031.
The Trump administration has sought to use more natural gas to power the A.I. data center boom while cracking down on wind and solar energy. Natural gas has become the largest source of America’s greenhouse gas emissions that are heating the planet. A report issued on Wednesday by the Rhodium Group, a research firm, found that U.S. emissions declines could potentially slow in the coming years because of faster data center growth and rising gas demand.
Brad Plumer is a Times reporter who covers technology and policy efforts to address global warming.“
The rapid advancement of artificial intelligence (AI) is being fueled by a massive build-out of data centers worldwide. This infrastructure boom, driven by the belief in the “Scaling Laws” of AI, is expected to lead to breakthroughs in drug discovery, robotics, and everyday AI applications. However, it also raises concerns about environmental impact, economic bubbles, and geopolitical divisions, particularly between the United States and China.
The milestones for artificial intelligence keep getting grander.
In 2023, an A.I. system passed the bar exam. In 2025, the technology helped scientists identify a suspected cause of Alzheimer’s disease. In May, A.I. had advanced so far that it solved a complex math problem that had stumped experts for 80 years. Last week, two A.I. systems under testing went rogueand hacked into a company’s database.
And this is still just the beginning.
From the American Midwest to the Persian Gulf, hundreds of major data centers now under construction will be turned on in the coming years. They are set to deliver an avalanche of computing power to develop and run A.I. that has no equal in the history of the technology industry, with breakthroughs that once felt revolutionary likely to become increasingly routine.
Behind each leap in A.I. are corresponding jumps in computing power. Today, there are about 20 million A.I. chips crammed into the data centers that underpin the technology’s growing abilities and usage worldwide, according to the research firm Epoch AI. That figure is expected to double roughly every nine months, putting the world on pace to have about 200 million of the chips by the end of 2028 — 10 times current levels.
Doubling every nine months
The number of A.I. chips, equivalent to H100 semiconductors made by Nvidia, currently in use by quarter, including near-term projections
150 million
H100 chip
equivalents
2026
Disproves planar unit distance conjecture — a mathematical problem unsolved since 1946.
2025
Identifies overlooked Alzheimer’s diagnoses and a potential cause
2024
An A.I. model outperforms the world’s top weather forecasting system
Note: The data here represents H100 equivalents as measured by chips in use.Source: Epoch AI.
In size and ambition, this moment compares to the building of the railroads in the 1800s, President Franklin D. Roosevelt’s New Deal in the 1930s, and the Manhattan Project to create an atomic weapon in the 1940s, technologists said.
“This is the largest scale infrastructure build-out in the history of humanity,” said Rob Wachen, a co-founder of the microchip firm Etched, which has raised more than $1 billion to meet the growing demand for A.I. components.
Peter DeSantis, who leads foundational A.I. models at Amazon — which provides computing power to the A.I. firms Anthropic, OpenAI and others — said the Seattle company has doubled its computing capacity since 2022 and would double it again by next year. “It’s hard to get your mind around the scale,” he said.
Fueling the surge is the belief that A.I. can take on more human responsibilities and solve increasingly complicated tasks with the more data and computing power you feed it. This tenet, sometimes called “the Scaling Laws,” has become the driving force behind this technological era. Those with the most computing power will create the most advanced A.I. systems, capturing the biggest share of profit and value, tech leaders argue. The biggest engine, they say, will win the race.
Confidence in the Scaling Laws has led A.I. leaders to make ever bolder predictions. Dario Amodei, the chief executive of Anthropic, has said that if these laws hold for another year or two, A.I. will be able to perform huge amounts of white-collar work. Demis Hassabis, the head of Google’s A.I. lab DeepMind, wrote recently that A.I. could usher in “10x of the Industrial Revolution at 10x the speed.”
Scientists and technologists see the coming deluge of computing power leading to drug discoveries and robotics advances, and industry analysts said it would drive more everyday use of A.I. in people’s personal and professional lives.
But the build-out has also stoked a backlash, spurring protests in many communities over how data centers could harm the environment, raise electricity prices and drain water. In the United States, data centers are shaping up as a major issue for November’s midterm elections, with a growing national movement pushing back against the tech industry and its billionaires.
The number of data centers in the United States is poised to explode
As more — and bigger — facilities become operational, they require much more energy.
Planned after 2026
Max: 750 megawatts
Average: 49 megawatts
Economists and investors have raised concerns that tech firms are spending faster than they can profit from A.I. Past infrastructure booms have been followed by downturns before the benefits of the technology were realized. The railroad boom in the 1800s, electrification in the 1920s and the dot-com bubble in the late 1990s were punctuated by economic recessions and a stock market crash as companies that overspent went out of business.
“Each time you’ve had a technological revolution, this kind of bubble bursting happened,” said Philippe Aghion, who won the Nobel in economic science in 2025 for research on innovation-driven economic growth. “A.I. is like the fourth industrial revolution and it has this aspect to it that generates a bubble.”
With more computing power coming online, geopolitical divisions are only set to widen.
The United States, home to about 5,500 data centers, about 10 times the next closest country, is far ahead of the rest of the world, including China. U.S. companies like Amazon, Google, Microsoft and Meta control about 80 percent of global computing power that drives A.I., according to Epoch AI. Google alone is believed to have four times as many A.I. chips as all of China’s companies, which are racing to catch up by developing new semiconductors and A.I. infrastructure of their own.
Amazon’s sprawling complex in New Carlisle, Ind., which is spread across an area that was once cornfields.AJ Mast for The New York Times
Lack of transparency in the A.I. industry makes measuring global computing capacity difficult, including the volume of chip supplies, total number of data centers and overall electricity consumption. The New York Times relied on estimates from groups including Epoch AI, Cleanview and SemiAnalysis that study the industry and publish widely cited forecasts.
For now, there are no signs that the spending on A.I. will slow. By 2029, A.I. infrastructure investment is forecast to top $1 trillion globally, up from $318 billion last year, according to IDC, the market research firm. That would be on par with the economic output of Switzerland, the world’s 20th-biggest economy.
The investment is worth it, said Jeff Dean, Google’s chief scientist, because it will power A.I. innovations and spread the technology’s use.
“You see capabilities emerge at larger scale that didn’t occur at smaller scale,” he said. “You’re also now trying to bring these capabilities to not just a few million users for a more niche product, but really to bring the capabilities to hundreds of millions or billions of users.”
‘An A.I. arms race’
2022GPT-4 ClusterDes Moines, Iowa
2024ColossusMemphis, Tennessee
2026RainierNew Carlisle, Indiana
2028FairwaterMount Pleasant, Wisconsin
In 2022, OpenAI used a data center tucked along Interstate 35 near Des Moines to build what would become ChatGPT.
Those origins now look modest. In 2024, Elon Musk’s xAI opened a far larger multibillion-dollar data center in a former appliance factory in Memphis, loading it with more than 100,000 A.I. chips and its own natural gas turbines.
Anthropic, whose recent model Mythos caused alarm among intelligence agencies for its uncanny ability to identify cybersecurity vulnerabilities, now uses seven interconnected data centers owned by Amazon that are fanned out across an Indiana cornfield.
Even larger data centers are taking shape. In Wisconsin, Microsoft is expanding what it said could become the world's most powerful data center. Once it is completed by 2028, it will be more than 100 times as powerful as the machines that trained that early version of ChatGPT.
A.I.’s growing abilities are linked to increases in the size of data centers. To create a cutting-edge model, huge amounts of computing power are needed to analyze and find patterns in data. That process, known as a “training run,” can cost hundreds of millions of dollars as tens of thousands of specialized chips churn through the data. Training works best when chips trade data over lightning-fast connections, and it can falter when hardware breaks down.
Once an A.I. model is finished, a wider network of data centers takes on a different role by providing the computing power for the system to field queries in real time. This work, known as “inference,” is increasingly driving the need for more data centers. As with a mail distribution hub, putting computing power closer to users enables A.I. models to think longer, respond more quickly and complete more complex tasks.
To meet that demand, production of semiconductors is also skyrocketing. In March 2024, the world had about 2.4 million of “H100 equivalent” A.I. chips, a unit of measurement that refers to the semiconductor that the chipmaker Nvidia released in 2022. Now as millions of more advanced chips are being brought online every month, the world is set to have about 200 million H100 equivalents by the end of 2028, according to Epoch AI.
H100, Nvidia’s GPU optimized to handle large artificial intelligence models used to create text, computer code, images, video or audio.Nvidia, via Reuters
The build-out could be slowed by troubles with chip and component manufacturing, financing and public opposition. Massive amounts of electricity will also be needed to support new data centers. Last year, the facilities consumed 64 gigawatts of electricity globally, roughly as much electricity as Germany consumes, according to SemiAnalysis, a market research firm. By the end of 2030, that is expected to quadruple, eclipsing the power used by all countries in South America and Africa combined.
At the most advanced A.I. data centers, every gigawatt of power equates to roughly $40 billion to $60 billion in costs, including servers, land, connectivity and utility hookups, according to industry estimates.
“These companies are essentially in an A.I. arms race,” said Carl Benedikt Frey, an economist at Oxford University. “If they don’t invest, they are acknowledging defeat.”
(The Times has sued OpenAI and Microsoft, claiming copyright infringement of news content related to A.I. systems. The two companies have denied those claims.)
The U.S. advantage
As more computing power arrives, American companies are expected to extend their A.I. lead. Amazon, Google, Microsoft, Meta and Oracle are projected to spend about $750 billion this year on data centers, chips and other A.I. infrastructure, up from roughly $400 billion last year, according to Goldman Sachs.
China, the next closest rival in A.I., is working to close the gap. Chinese companies had roughly 1.16 million H100-equivalent chips at the end of 2025, up from roughly 244,000 at the beginning of 2024, though those figures exclude smuggled chips and other offshore computing resources used by Chinese firms, Epoch AI estimated.
How the computing power of U.S. and Chinese data centers compares
The number of H100 chip equivalents for the largest American data center in operation now versus the largest one in China.
124,000 H100 equivalents
1.1 million H100 equivalents
Note: Each block represents 10,000 H100 chip equivalents.Source: Epoch AI.
China’s National Energy Administration has estimated the country’s electricity use for data centers will reach the equivalent of around 91 gigawatts by 2030, or about 6 percent of total use, up from 19 gigawatts last year.
China has been hamstrung by export controls and other limits initiated by the United States on A.I. chips and other key technology. To address those hurdles, Beijing has made A.I. infrastructure a national priority. In March, the Chinese Communist Party released a five-year economic strategy that mentioned A.I. more than 50 times and called for building an interconnected network of data centers to create “next generation supercomputing.”
Xi Jinping, China’s president, has positioned China as a counterweight to American tech dominance. “A.I. development should not be a solo performance by a single country, but a symphony of international cooperation,” he said in a recent speech in Shanghai.
China’s top tech companies are building new A.I. chips and data centers. This year, Huawei, ByteDance and Alibaba are expected to spend $111 billion on data centers and other A.I. investments, according to Bernstein Research.
Even with these challenges, Chinese start-ups like Moonshot AI and DeepSeek have built powerful A.I. models. Often given away as “open source” software, which others can freely use and modify, they are growing more popular. Many users regard the Chinese models as good enough, more efficient and cheaper than leading U.S. models.
Still, everyday use of A.I. is limited across China right now because of lack of computing infrastructure, said Jordan Nanos, an analyst at SemiAnalysis.
Mr. Nanos said the U.S. data center lead over China would likely grow over the next four to five years, before China’s domestic chips are produced at scale. After that, China should begin closing the gap.
“The advantage will run out,” he said.
The U.S.-China race threatens to leave the rest of the world behind. France, Germany and other nations are trying to encourage data center construction across the European Union, which has 5 percent of global A.I. computing power, according to a report by A.I. developers and policy experts in the region. Europe has been hampered by electricity and land access, permitting and financing.
In the Persian Gulf, where Saudi Arabia and the United Arab Emirates have pledged billions to build data centers, the war in Iran has affected plans.
A model of a data center complex under construction in the United Arab Emirates, which stems from a joint venture between G42, Microsoft and OpenAI.Giuseppe Cacace/Agence France-Presse — Getty Images
Fears that A.I.’s economic gains are unequally spread are growing, with the world potentially splitting between those with the infrastructure to utilize the technology and those without.
“If 75 percent of the compute today is in a few postal codes in the U.S., 12 to 15 percent in China, and 5 percent in the E.U., where does it leave the rest of the world?” said Amandeep Gill, under secretary general at the United Nations who is the special envoy on tech issues.
The accelerating A.I. loop
To those in the A.I. industry, adding huge data centers is akin to outfitting a car with a jet engine.
Signs of that acceleration are already here. Uber, PepsiCo and Walmart are increasingly turning work over to A.I. “agents,” the bots that can perform a growing list of multistep tasks like coding, compiling research reports, handling customer support and reading and responding to emails.
With more computing power, agents can take on more responsibilities, said Google’s Dr. Dean, who has worked in A.I. research for more than 30 years. He envisioned a scientist asking hundreds of A.I. agents to autonomously devise and test various hypotheses in biological research and then taking the best leads to build off.
Under at least one new assessment, the Remote Labor Index, A.I. models have become increasingly capable. The test examines their ability to do common freelance tasks, like building a mobile video game. In October, leading models completed 2.5 percent of tasks. By July, Anthropic’s Fable A.I. model completed 16 percent.
These jumps in abilities have economists warning about major changes to the labor market.
“There’s going to be millions of jobs destroyed, millions of jobs created,” said Erik Brynjolfsson, an economist who is the director of Stanford University’s Digital Economy Lab. “That’s going to be very difficult. Even if new jobs are created, they’re not the same jobs.”
Leading A.I. labs are continuing to race ahead. One long-sought breakthrough, called recursive self improvement, would allow A.I. to speed its own progress with little or no help from human developers. An A.I. model would essentially help build the next version of itself.
Google is already exploring various kinds of self-improvement tools. A process that once involved dozens of A.I. researchers testing hundreds of ideas could eventually be turned over to thousands of “very tiny models,” which come up with ideas on their own, Dr. Dean said.
With more computing power coming, “you can fully automate the loop,” he said. “We are at the beginning stages.”
“AI is not replacing photographers equally. It is changing the market value of every skill the profession is built on, separating those that were tied to the camera from those that will matter even more.
Ordinary photographs were always replaceable. Labor just hid it. A competent landscape took skill, time, and equipment, and that effort was treated as value even when the image itself was interchangeable. The market paid for the labor, not the photograph.
Then came stock libraries. The value of acquiring an ordinary image dropped sharply: the labor stayed, but the volume of potential sales grew while the price of each purchase collapsed.
AI zeroes out the labor, and the interchangeable image is worth exactly nothing. AI did not take the value away. It made the absence of value visible. And it did this not because it learned to make better images than photographers. It simply stopped counting the cost of producing them as part of the image's worth.
So what survives, it seems, is what cannot be reproduced. Not by the measure of craft, but because it is tied to a single event, to a trace, to an encounter that cannot be generated, because it did not originate in a written concept. This is where photography and generation supposedly part.
The argument holds only while we picture AI as a system that generates images. All of it rests on the photographer's work of hunting for the unique moment. But why can't what cannot be generated be delegated to AI instead?
Not the AI that makes images, but the one that drives the cameras and assesses the situation, the probability of a moment or an event. Or simply records everything at once. If a system can judge the odds of an event better than a person, can take a better vantage point, and can capture continuously, photography's old line of defense starts to fall apart fast.
The need to hold a camera in your hands may shrink sharply in the near future. Not because of generated images, but because of new mobility technologies applied to the same cameras, and most likely because of both at once. Not just to change the angle, but to take the photographer's place in physical space. Today we argue about generating images. In a few years the central question may no longer be generation, but the delegation of the act of shooting itself.
The camera can be handed to the machine entirely. One question remains: what happens to the knowledge a photographer spent decades building? Does it vanish with the camera, or does it just change its field of application?
Go back to the words about a written concept, the prompt. Here is the curious part: precise knowledge of composition and technical language produces far more accurate, more realistic images. Knowledge of the zone system, how spatial layers work, how depth of field and exposure behave, how to place light, and when to release the shutter shapes a generated image far more precisely than mere "vision." A paradox follows. The deeper a person understands photography, the stronger their command of generative systems. AI does not cancel professional knowledge. It moves where that knowledge applies. Apply the technical skills you have used for years to crafting the prompt, the exact description of the result you want, and it will stand head and shoulders above an amateur's. A professional photographer does not describe the picture he wants. He describes the photographic process that has to arrive at it.
Return to the word we started with. A written concept is intention stated in advance. The prompt does not search for the result; it retrieves one that already exists. Shooting works the other way around. Intention and result do not match, because the world intervenes, and the best thing in the frame is often the thing you never planned. These are two different modes: to describe in advance and to discover in the process.
A professional is strong in generation not only because he describes it better. He is strong because all his life he trained something else: to recognize the moment when the result outran the intention. That skill grew out of shooting, out of catching the unplanned. And it carries over into the place where everything is supposedly fixed in advance.
Generation does not level the amateur and the professional. It pushes them further apart. The one without the language writes "a beautiful sunset" and gets exactly what everyone else gets. The one who understands light, foreground, midground, background, and the moment frames a request the amateur simply cannot put together. He simply does not know what there is to say.
Generative images hand us endless combinations. The difficulty appears on the far side of the shot, where you no longer choose the moment but choose the variant that will be final. With the power to spin variations of the same frame forever, when do you stop? When do you make the final choice? The hard part of the profession used to sit before the image. Now it moves, step by step, to after it.
Photographers know a version of this already: pulling the three or five best frames out of a thousand. But it grows far sharper, and demands far more, once endless improvement opens in front of you. Before, the photographer chose among frames that already existed. Now he also has to choose the moment to stop refining. That is a different kind of professional decision.
The photographer who now looks vulnerable from every side is in truth vulnerable in only one narrow place. The same place that always made photography one of the cheapest, lowest-paid trades on the market. That place is the camera operator. The person who exposes correctly. He is no longer needed. But the problem may run deeper. For too long we used the word "photographer" for several different professions at once.
One person searched for the subject, made the aesthetic decisions, ran the camera, edited, selected, printed, and sold the result. We got used to calling this a single craft. It looked single for one reason: all of it lived in one body and was paid with one fee. The client paid for the shoot as a whole and never saw that inside that sum some roles were worth a great deal and others almost nothing.
The fusion was an economic convention, not the nature of the profession. As long as the roles could not be separated, their prices stayed the same. There was simply no way for the difference to show. Running the camera, finding the frame, the selection, and the decision about what the image should be all went on one invoice, though they asked for completely different things and were worth completely different amounts.
AI does what the market never could. It uncouples the roles and, for the first time, prices each one on its own. And the moment it does, the same gesture from the beginning repeats itself. The market once stopped counting the cost of production as part of the image's worth. Now it stops counting the operator's work as part of the photographer's worth. The first role to disappear is the most technical one, the role that passed for the heart of the craft because it needed hands and equipment and so seemed central.
But the curator and the visionary, those invisible layers above the operator's work that always stood behind a successful photographer, go nowhere. If anything, a feel for fine settings, for relationships, for style and composition only sharpens and gains weight. These were always the qualities that set apart photographers whose work could not be reproduced by simply repeating the technical process.
What is striking is that AI takes away the chance to make a good photograph by accident and become known for it. The chance we still see at global photo contests, where the winners are excellent works by authors not yet twenty. That stroke of luck disappears once generative technology and camera control change radically.
Maybe that chance is what we are really mourning. If so, the argument about AI turns out to be no argument about technology at all. It becomes an argument about which part of the profession was ever truly ours, and which part only needed human hands for a while.
AI raises the stakes. It asks for far more professional competence than many who call themselves photographers actually have.“
“Samsung’s new foldable phones, the Galaxy Z Fold 8 and Z Fold 8 Ultra, are more expensive than ever, with the Ultra model reaching $2,700 for 1TB of storage. The price increase is attributed to a RAM shortage and higher component costs, impacting the entire tech industry. Despite improvements in design and features, the rising cost of these premium devices may make them less accessible to many consumers.
Commentary: Samsung's newly unveiled foldables are better — and pricier — than ever. Who can afford it?
David Lumb is a managing editor for the mobile team, covering mobile and gaming spaces.… Read full bio
Samsung just unveiled its best foldables yet, but they’ve all gotten price hikes over last year’s models. Folding phones have always been the most premium devices — with price tags to match — but this year’s crop is something else.
I was aghast when the first foldables debuted around $2,000 years ago, and they haven’t gotten more affordable. Even though the starting price has stayed the same, these phones are still out of reach for many phone buyers, especially in these financially strained times.
Those prices are just for the minimum 256GB minimum for storage. If you want more room for photos, videos and apps, expect to pay out the nose: A fully kitted out Galaxy Z Fold 8 Ultra with the maximum 1TB of storage is $2,700, which is stunning, considering that the Samsung Galaxy Z TriFold released in January was only a bit more expensive at $2,900. For a couple of hundred bucks more than the highest-tier Z Fold 8 Ultra, you can get an inner screen that’s 25% bigger.
The Galaxy Z TriFold was on sale for less than three months in early 2026.
Patrick Holland/CNET
Or rather you could have, but Samsung pulled the plug on sales of TriFold phones back in March. At the time, Samsung said that the TriFold was “a super-premium device in limited quantities.”
Personally, I’d bet this and the Z Fold 8’s rising price have the same culprit: the RAM shortage, which is making seemingly every tech product more expensive. It’s not hard to draw conclusions from the rising cost of memory and components and a shuttered product.
Given these conditions, it’s not surprising that Samsung has followed other products (and even its earlier phones, like the Galaxy S26 series) in raising prices by $100 or more over last year’s models. Samsung itself said that higher prices for the Z-series phones reflect increased memory and component costs, which have affected the larger tech industry. The company also pointed to improvements over last year’s devices, including the new Titanium Flex displays, larger batteries and better 50-megapixel ultrawide cameras on the Z Fold 8 and Z Fold 8 Ultra.
So how does a fully kitted-out Galaxy Z Fold 8 Ultra get up to $2,700? Because of another typical practice, which is for phone-makers to boost prices of the higher storage tiers. In fact, last year’s Galaxy Z Fold 7 topped out at $2,500 for 1TB of storage.
That was still a lot of money, but there’s something bleak about the rising cost of memory, further skyrocketing the prices of the most advanced phones on the market. Back in 2017, the iPhone X crossed the phone-price Rubicon by launching at $1,000; in 2019, the original Samsung Galaxy Fold debuted at just under $2,000.
The Galaxy Z Fold line has come a long way since then, with a litany of durability, design and feature improvements. You can still get the latest Z Fold 8 Ultra for $2,100 if you don’t mind the minimal 256GB storage without the capability to expand it using a microSD card. But with a maximum price creeping toward the $3,000 milestone with little increase in functionality, the Z Fold 8 Ultra might be a harbinger of diminishing affordability at the top end of smartphone lineups.
As the RAM shortage continues driving up product prices, either everyone will have to live with a lot less onboard storage — or fancy foldables will continue to be priced out of everyone’s hands.
David Lumb
Managing Editor, Mobile
David Lumb is a managing editor for the mobile team, covering mobile and gaming spaces. Before CNET, he covered tech, gaming, and culture for TechRadar, Engadget, Popular Mechanics, NBC Asian America, Increment, Fast Company and others. As a true Californian, he lives for coffee, beaches and burritos.“
The U.S. Is Still Winning the A.I. Race. Trump Could Blow It.
“The U.S. leads in semiconductor technology, crucial for A.I. development and national security. Export controls on advanced chips to China are vital to maintaining this advantage. President Trump’s relaxation of these controls could jeopardize U.S. leadership in the A.I. race, potentially empowering China’s A.I. capabilities and cybersecurity threats.
The editorial board is a group of opinion journalists whose views are informed by expertise, research, debate and certain longstandingvalues. It is separate from the newsroom.
Americans often hear about China’s having surpassed the United States in the manufacturing of emerging technologies, including clean energy, electric vehicles and drones. Semiconductors are an important exception. In partnership with its allies in Europe and Asia, America designs and makes by far the best computer chips in the world.
Those chips are crucial to America’s economy and national security. They have enabled the development of the world’s leading technology companies and allowed the military to create advanced weapons. With artificial intelligence, these chips have become even more important. They are the engines that power frontier A.I. models.
China is working hard to catch up, and the United States should take steps to keep its advantage. Most important, it should continue to prohibit American companies from selling the most advanced chips and equipment to China. Over the past decade, presidents of both parties have worked with Congress to enact export controls. Those controls have been “existentially important,” Dario Amodei, the chief executive of Anthropic, the A.I. company, has said.
In his second term, however, President Trump has gone the opposite direction. He has relaxed controls on some advanced semiconductors and suggested he may go further. Nvidia, an American company that makes the most advanced chips, has lobbied for the ability to sell more to China and is likely to continue pushing. Yet this is a classic case in which a company’s interest runs counter to the national interest.
The new A.I. model Mythos, from Anthropic, is a clear example of why it is so important for the United States to remain in the lead in the artificial intelligence race.
Mythos is so powerful that many experts believe that it presents a cybersecurity threat to governments, companies and individuals. Hackers could potentially use it to penetrate banks, hospitals, energy grids and communication networks. In response, Anthropic has released Mythos to some government agencies and a small number of companies, giving them a chance to understand their vulnerabilities and improve their defenses. Developers for the Firefox web browser said they were able to fix more security bugs in one month with the help of Mythos than they did in all of 2025.
Now imagine that a Chinese company had obtained a model like Mythos first. Over the past decade, hackers linked to China have broken into the computer systems of the U.S. government, Microsoft, a large health insurer and a major credit agency. A proxy group known as Volt Typhoon has attempted to install malware inside American water and electricity systems to give China the ability to disrupt them. Mythos could have expanded these efforts, which ultimately serve the authoritarian aims of the Chinese Communist Party.
The A.I. race between China and the United States has recently narrowed. On July 16, a Chinese start-up, Moonshot AI, released a model that matched Anthropic’s best publicly available model in some capabilities even though it remains behind overall. Tellingly, Michael Kratsios, the current White House science adviser, accused Moonshot of illicitly gaining access to Nvidia technology in Thailand as part of developing the new model.
China has two advantages in the semiconductor race: far more people, including scientists, than the United States; and abundant electricity for data centers, thanks to its rapid build-out of energy infrastructure. Chips are China’s main choke point. Its best semiconductors remain well behind Nvidia’s best publicly available line of chips, known as Blackwell, and also behind even Nvidia’s next most powerful line, the H200.
The gap reflects both America’s scientific capabilities and the success of federal policy. Mr. Trump deserves some credit for that success. In his first term, he imposed new export controls against China. President Joe Biden significantly strengthened the controls. Members of both parties in Congress backed the actions, and some House Republicans criticized Mr. Biden for being too soft.
China’s leaders and technology executives understand how effective the export controls have been. “Money has never been the problem for us,” said Liang Wenfeng, the chief executive of DeepSeek, one of China’s most advanced A.I. companies. “Bans on shipments of advanced chips are the problem.” Chinese Premier Li Qiang similarly acknowledged that “insufficient supply of computing power and chips” was slowing A.I. development.
Nvidia and other critics of the export controls make an unpersuasive counterargument. They claim that the policy encourages China to build its own semiconductor industry instead of remaining dependent on America’s chips. But China’s leaders obviously recognize the importance of developing their own industry. They lack not the motivation to do so, but the technical ability. Giving them the world’s most advanced chips, as they desire, would enhance their ability to develop their own version and beyond.
Some analogies expose the weakness of the counterargument. During the Cold War, Washington did not provide the Soviet Union with nuclear technology to prevent it from developing its own weapons. Nor does the United States today share other forms of the most advanced and sensitive technology, such as weapons systems, with China, even though doing so could increase the sales of the American companies that make the technology.
Fortunately, Mr. Trump’s softening of American policy has not yet had significant real-world effects. He has allowed China to buy H200 chips, Nvidia’s second-best line, but no sales have yet gone through. China seems to be holding out for access to the top line, and American officials would still need to approve each sale individually.
The best policy going forward would be a strengthening of the controls. Regulators should avoid approving any H200 requests unless they become confident that H200 would not strengthen China’s A.I. capabilities. The Trump administration should maintain the ban on Blackwell sales and should extend it to the next generation of chips, known as Rubin.
The administration should also fix loopholes that China has occasionally used to get around the controls. One example is the construction of data centers with advanced chips in countries that do not face export controls, such as Malaysia and Singapore. Working with Congress, the administration should increase budgets and staff at the Bureau of Industry and Security to closely track American-made chips.
And the Trump administration should stop its destructive approach to American alliances. This country’s semiconductor advantage depends on an intricate supply that involves Japan, the Netherlands, South Korea and Taiwan. Without these partners, the United States would lack the ability to turn innovative American designs into actual products.
At a time of national self-doubt, Americans can feel pride about our world-leading semiconductors. During the Cold War, the United States fell behind the Soviet Union in the space race and needed the 1957 launch of Sputnik to inspire a comeback. Another way to look at that story, however, is that the Soviets took their lead for granted and lost it.
This time, America has produced its own kind of Sputnik. We should not squander it.
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