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As the Iran war drains vital stockpiles of missile interceptors, American allies increasingly question Washington’s capacity to wage multi-front conflicts

Ever since President Trump’s return to office, America’s allies have been fretting about Washington’s intentions. Now, after the inconclusive war against Iran has eroded U.S. weapons stockpiles and laid bare the limits of American hard power, they also worry about American capabilities.

Can America still defend its friends, if it wants to?

The question that officials in allied—and adversary—capitals are asking themselves is whether industrial decline, strategic incoherence and political dysfunction have weakened the U.S. to the point where it would no longer wield decisive force in a multi-front conflict that may encompass Europe, the Middle East and Asia. It is a concern shared by many in Washington, too.

“If you think about deterrence, it’s about capability and credibility,” said strategist Matthew Kroenig, a senior fellow at the Atlantic Council who served in the Pentagon during the first Trump administration. “The question used to be credibility—will the United States really do it? But capability is an important part of this equation. And, with all the reporting about how the U.S. is expending munitions, it’s just logical that adversaries would assume that the U.S. is less capable now than it was several months, or several years, before.”

Trump’s decision to scale back military exercises with South Korea and to describe North Korea’s dictatorship as “unthreatening and respectful” just as Pyongyang is deploying ballistic missiles and troops to Europe as part of its alliance with Russia is the latest move that has raised doubts about American staying power.

The overture to Pyongyang came alongside a monthslong drive to accommodate China, including a freeze on $14 billion in U.S. military sales to Taiwan—a reversal of Trump’s first-term priorities. Meanwhile, crucial U.S. military assets, from missile defense batteries to the sole aircraft carrier still deployed in the Pacific, have been moved from Asia to deal with the Middle East crisis.

While paying lip service to the NATO alliance in public, European officials are increasingly doubtful in private that Trump’s America will help them in case Russia attacks. “Unofficially, we all know that they won’t risk their security for the sake of the Baltic states, or for the sake of Germany,” said retired colonel Roderich Kiesewetter, a lawmaker from Germany’s ruling party. “The American nuclear umbrella is gone.”

The growing sense of American—and Western—vulnerability can tempt autocratic adversaries, particularly Russian leader Vladimir Putin, warned Nico Lange, a former senior German defense official who now runs the Iris think tank. “Iran is increasingly humiliating the United States in front of everybody, while the U.S. is creating uncertainty about their commitment to European security, and the Europeans aren’t showing any will to deter Russia,” he said. “All this creates an opportunity structure for Putin that is extremely dangerous.”

The White House has pushed back on reports of weapons shortages caused by the war in Iran. “The United States Military has more than enough munitions, ammo and stockpiles to serve all of President Trump’s strategic goals and beyond, and Operation Epic Fury has exposed what happens when you mess with the United States,” deputy press secretary Anna Kelly said. “Even still, the President has urged our defense contractors to constantly produce more made-in-America weapons, which are the best in the world.”

Military analysts, however, calculate that, in some vital categories, stocks are running dangerously low and will take many years to replenish. The Center for Strategic and International Studies has estimated that the U.S. has already burned through half of its prewar stocks of missile interceptors, such as Patriots and Thaads, and has used up one-third of the arsenal of Tomahawk and JASSM long-range missiles.

This has direct implications beyond Iran. A Council on Foreign Relations expert study released this month said that “the U.S. weapons arsenal has been depleted and no longer has the capacity to deter a Chinese attack against Taiwan.” It added that Washington needs to immediately put mass production of precision missiles on a wartime footing because “waiting until conflict begins will be too late.”

Asked about these calculations, the Pentagon replied with a statement by chief Pentagon spokesman Sean Parnell. “Claims of U.S. munitions shortages are false,” it said. “America’s military remains the most powerful fighting force on earth. We have everything required to strike at the time and place of the President’s choosing.”

Yun Sun, director of the China program at the Stimson Center in Washington, said that U.S. denials of missile shortages aren’t surprising. “America will never acknowledge that we don’t have enough ammunition. It will assert that it has the ability to walk and chew gum,” she said. “But deterrence is in the eye of the beholder. What if Taiwan perceives that you don’t have the capacity and the will to defend it, and that its best option is to be coerced by China and to start negotiations over unification?”

China and other potential adversaries would be foolish to try taking military advantage of this shortfall, said Michael Fullilove, executive director of the Lowy Institute in Australia. America, he said, remains the pre-eminent global power, with the financial, technological, cultural and demographic muscle to rebound.

“When people say America is weak, Vladimir Putin would love to be as weak as America. Xi Jinping would love for China to be as weak as America,” he said. “I don’t want to be Pollyannish, but when I squint my eyes I see American strength more than American weakness.”

Unlike in Europe, where defense officials increasingly fear the prospect of Putin taking the war beyond Ukraine, officials and analysts in Asia don’t see a full-scale war on the horizon just yet.

“People will worry, of course, but these munitions will be restocked. And in the meantime, there is really very little probability of a major conflict breaking out in East Asia,” said Singaporean political analyst Bilahari Kausikan, the island nation’s former ambassador to the United Nations. “The Chinese have a huge number of domestic problems to deal with first, and with Taiwan in particular, they are not gamblers. In all the potential flashpoints in Asia, nobody thinks that a big conflict is imminent.”

In all these actual and potential conflicts involving U.S. allies and partners worldwide, the critical shortage of ballistic-missile interceptors, particularly the Patriot PAC-3 MSE, has emerged as an Achilles’ heel for the entire free world—and its partners.

Part of the imbalance in the missile threat is that the autocracies—those actually at war, like Russia and Iran, and those not yet, like China—have already put their defense-industrial base on a war footing, achieving rapid increases.

The West lives according to peacetime rules, in which corporate calculations and shareholder interests often trump national-security needs. “This is the existential problem: our foes think they are at war, while we pretend that we are not,” said a senior European defense official.

CSIS has estimated that the U.S. now only has 800 Patriot missiles left in its arsenal. Despite the rapid consumption by Ukraine and multiyear backlogs, interceptor manufacturers have only achieved a modest boost in production so far. Lockheed Martin said it delivered 620 PAC-3 MSE interceptors last year, an increase of more than 20% from the year before. Russia’s missile production, by contrast, has surged since 2022, despite Western sanctions.

This disparity has strategic consequences. Ukraine has already been essentially left defenseless against Russian ballistic missiles salvos, as Trump declined Kyiv’s repeated requests for more Patriots. This, in turn, has added to Putin’s determination to continue the war, as Russia counts on missile strikes to destroy Ukraine’s energy infrastructure in the coming winter, forcing Kyiv to capitulate.

In the Persian Gulf, states like Saudi Arabia have opposed a resumption of a full-scale U.S. campaign against Iran because their own interceptor stocks are too low to protect vital infrastructure against renewed Iranian missile barrages. European nations, while working on their own missile-defense projects, remain years away from credible protection against Russian—or Iranian—missiles.

“There is now a risk of an acute crisis across different theaters brought about by the shortage of interceptors and missile defense,” said John Bew, who served as foreign-policy adviser to British prime ministers in 2019-2024 and is now a professor at the War Studies Department of King’s College London. “This has immediate implications for the Gulf, of course, but is increasingly understood as a major shortfall in European defense and has potentially major consequences in Asia, too.”

Russia alone now manages to produce more than 100 ballistic missiles a month, according to intelligence estimates, and a similar number of cruise missiles. The overall production of ballistic missiles by the axis of adversaries that also includes China, North Korea and Iran outmatches the manufacturing of interceptors in the West by several times. (Making the math even more alarming, shooting down one ballistic missile usually requires two or even three interceptors.)

“We in the West have made a strategic planning mistake in not calculating interceptors against ballistic missiles that are produced,” said Lange, the former German defense official. “We have a problem in ballistic missile-defense production, but they don’t have a problem in ballistic missile production—not in Russia, not in China, not in North Korea.”

Ballistic missiles have become an instrument of geopolitical coercion because of technological change. Previous generations of ballistic missiles were so costly and imprecise that they were mostly considered useful in the nuclear context, in which a deviation of hundreds of yards from a target didn’t matter because of the mass devastation caused by a nuclear device. Up until the U.S. withdrew following the Sept. 11, 2001 attacks, a Cold War-era treaty strictly limited antiballistic missile defense capabilities to maintain the equilibrium between the two superpowers.

Now, however, even middle powers like Iran have developed precision ballistic-missile capabilities. Iranian missiles managed to inflict serious damage on U.S. military bases in the Gulf and Jordan, as well as energy and industry targets in Gulf nations and Israel. Though Iran’s missile forces have been degraded by Israeli and American bombing, they have yet to be fully suppressed.

“In the environment of major interstate war, even substantially weaker adversaries have enough capability to hurt, enough capability to launch these ballistic missiles. And even the United States, with all of its financial and industrial power, is not able to keep up structurally,” said missile expert Fabian Hoffman, a senior fellow at the Norwegian Institute for Defense Studies. “Ballistic missile defense, as it was conceived, has just been exposed as something that doesn’t work that way in practice.”

Interceptors able to counter modern ballistic missiles are among the most sophisticated and expensive military systems, which is why only a handful of companies worldwide can make them. That is a huge difference with the parallel revolution in drone warfare: drones, and systems used to intercept drones, including air-defense drones, are much simpler and cheaper.

The U.S. and allies now are pumping hundreds of billions of dollars into missile defense and long-range strike programs. The Pentagon’s $59 billion deal announced with Lockheed Martin last month would triple Patriot PAC-3 production by the end of 2030. The Pentagon has also awarded Lockheed Martin a $35 billion seven-year contract to quadruple Thaad interceptor production. Both of these contracts have yet to be fully funded.

There is a certain silver lining in the fact that the shortfall in missile defense has been exposed in a war against a relatively weak foe like Iran, argued Ludovic Hood, a senior fellow at the Hudson Institute who served in senior State Department positions involving Ukraine and the Middle East until earlier this year.

“The wake-up call was necessary. It’s been very easy for Western powers in recent decades to be lulled into a sense of false security,” Hood said. “As unpleasant as it is to have to go to war, it can actually be a useful forcing function to make sure that we’ll be prepared for something much more strategically challenging in the future.”

University lab nurtured the computer scientists who are using ingenuity and imitation to chase down Anthropic and OpenAI; ‘they know perfectly how to monetize their work’

Computer geeks Tang Jie and Yang Zhilin were mere teacher and pupil more than a decade ago, with dreams of building a machine that could think like a human. Today, they are two of the Chinese artificial-intelligence tycoons who have U.S. AI pioneers looking over their shoulders with alarm.

Tang, a professor at Tsinghua University, co-founded Z.AI, one of the companies hot on the heels of Anthropic and OpenAI in capability and global usage. Yang, whom Tang once nominated for his university’s top academic award, leads another, Moonshot AI. Each company is valued at tens of billions of dollars.

The rapid rise of Chinese AI models has sparked a host of questions in both Silicon Valley and Washington. How did Chinese AI labs get so far so fast? Where did their AI models come from? Are companies like Tang’s and Yang’s stealing from U.S. models, and should their products be banned to protect American national security?

The careers of Tang and Yang show that China’s AI push is nothing sudden. Tang, 49, has been working on machine learning for about a quarter-century. More than a decade ago, he was among the Chinese AI experts already at the forefront of using computers for humanlike tasks such as understanding language and recognizing faces.

Tang, Yang and their fellow researchers—many of whom got started at Tsinghua and a few other institutions—make up a virtual Silicon Valley in China’s tech centers. Knowledge flows easily between them thanks to a culture of publishing research and the Chinese developers’ preference for open-source technologies that are largely free for download and modification.

To catch up in the era of generative AI, Chinese companies have relied on the familiar mix of ingenuity and imitation that also lies behind the nation’s successes in electronics, cars and green energy. Chinese AI developers brought back home U.S.-trained computer scientists and closely followed advances in the U.S.

More controversially, some appear to have “distilled” U.S. models to help train their own faster, employing a process in which a new system learns from an existing one by asking it hundreds of thousands of questions and analyzing the answers.

Anthropic has accused Chinese companies, including both Z.AI and Moonshot, of violating its policies by engaging in large-scale distillation. Neither company has commented on the issue. People working at Chinese model developers acknowledge that distillation is widespread globally and that some Chinese companies might use it more aggressively than American counterparts.

Elon Musk predicted in June that China wouldn’t match Anthropic’s top-of-the-line Fable model until the first quarter of 2027. Tang fired back on X: “Won’t take that long.”

Industry leaders in both countries said the capability of China’s best AI model is just months behind that of the best in the U.S.

This month, Z.AI released its latest GLM-5.3 model, saying it has matched Anthropic’s Mythos 5 in cybersecurity capabilities.

China’s government has long viewed AI and computer science as important drivers of economic growth and national security. Its catch-up formula involves both state support and private-sector competition. The state has poured money into research at universities such as Tsinghua and funded startups, and central and local governments have been early customers for the startups’ services.

After Anthropic introduced its Mythos model this spring, which can detect cybersecurity flaws, some Chinese officials likened it to a nuclear weapon of the AI era. Aware that Washington can block foreigners from getting the top U.S. models, Beijing is supporting domestic AI developers through a national fund and relaxing rules to help startups list shares in public markets.

Nearly half of total equity-capital investment in China flowed into AI in the first half of this year, most of it from government-backed funds, according to the Investment Association of Central State-Owned Enterprises.

Talent pool

In the early 2000s, China’s internet industry took off and began generating troves of data as millions of users started shopping, searching and communicating online. Companies began investing in machine learning to make e-commerce more efficient and analyze users’ online behavior.

The government, looking for ways to surveil the population and detect threats, sought computer programs that could identify faces and voices. China’s universities turned out students specializing in computer vision, a branch of computer science that tries to teach machines to see as humans do.

That era produced the “four little dragons of AI,” computer-vision startups including SenseTime that trained many of the engineers active in AI today.

Money and talent poured into Tsinghua, the country’s flagship university for science and technology. In 2005, Andrew Yao, a former Princeton professor and winner of the award named after computer-science pioneer Alan Turing, established an elite “Yao class” at Tsinghua to cultivate top-tier talent.

Tang, the Tsinghua professor and Z.AI co-founder, has been at the university since his Ph.D. years. He studied data mining, a discipline that seeks to extract hidden patterns from large data sets and later became foundational to AI.

Tang told technology writer Mehran Gul, according to his book “The New Geography of Innovation,” that after getting his Tsinghua doctorate, “I thought I could do something big in China and also in that way maybe I can help China better. So I decided to stay.”

Tang enjoyed testing his endurance by running marathons and participating in triathlons. He ran his lab the same way.

“Prof. Tang’s team is notoriously intense,” said Kevin Zhong, who briefly worked with Tang during his master’s studies at Tsinghua and now works in London. The professor always pushed his team to test potential machine-learning techniques with experiments, Zhong recalled, and researchers sometimes stayed in the lab until 3 a.m. to finish their tests.

“They have a real knack for consistently getting published in top-class journals, and they know perfectly how to monetize their work,” Zhong said.

By the 2010s, Tang’s lab had a national reputation among aspiring techies, and his former students began populating top companies in Silicon Valley. Yang, the future Moonshot founder, began intensive training in programming at age 17, competing in China’s National Olympiad in Informatics. At Tsinghua, he developed machine-learning algorithms with Tang.

In 2018, Beijing cleared the way for researchers at state institutes and universities to start companies and market their technology. The next year, Tang spun the company now called Z.AI out of his Tsinghua lab, funding it in part with a data-analytics platform he ran that had clients such as Google and IBM.

Long before the U.S.-China AI battle became headline news, Tang saw himself as competing with OpenAI’s Sam Altman. When OpenAI released its pioneering GPT-3 model in 2020, Z.AI aimed to produce something as good.

“I am putting all my effort into artificial general intelligence with the mission of teaching machines to think like humans,” Tang wrote on his personal website several years ago.

After studying with Tang, Yang earned a Ph.D. at Carnegie Mellon University in Pittsburgh. He returned to China to help with his former professor’s research while deciding to set up his own company.

An avid rock drummer, Yang called his startup “Dark Side of the Moon” in Chinese, riffing off the Pink Floyd album. For its English name, he chose Moonshot.

In Shanghai, two more former Tsinghua researchers, including one from the prestigious “Yao class,” also started their own companies to build AI models.

A final entrant was DeepSeek. Its founder, Liang Wenfeng, got his start in the computer-vision field two decades ago and went on to found a hedge fund built around using AI to analyze financial markets. In 2023, he spoke with Tang, who later told his staff he was impressed by Liang’s “different ways of thinking.” That year, Liang spun out DeepSeek from his hedge fund, declaring the same goal as Tang’s: humanlike intelligence.

The startup founders came from all corners of China—Tang was from a remote part of Sichuan in the west, while Yang and Liang hailed from the entrepreneurial south. They clustered in a few cities including Beijing and Hangzhou, where Liang went to school and started his business.

More with less

The startup founders knew China’s shortcomings compared with the U.S.: lack of funding and lack of access to the most advanced AI chips, owing to Washington’s export controls.

Tang raised around $1.25 billion by mid 2025 from investors including Chinese tech giants and Hong Kong-based venture capitalist Neil Shen. State-backed funds also wrote checks, heeding Beijing’s call to nurture homegrown AI companies. Yet such numbers paled in comparison to the billions of dollars that OpenAI’s Altman and Anthropic were bringing in.

Investment bank Jefferies would later calculate that Chinese tech companies during these years were investing less than one-fifth the amount of their U.S. counterparts. They had to do more with less.

Liang’s team at DeepSeek came up with some of the most important workarounds, including multihead latent attention, or MLA. That technique slashes an AI model’s memory usage—such as in a chatbot conversation—by creating a shorthand version of what has gone before. Using less memory saves computing power and money.

DeepSeek was among the earliest adopters in China of a model design called “mixture of experts,” or MoE, in which an initial routing mechanism directs the problem to a specialized expert model—akin to a head chef directing a spaghetti order to the kitchen’s Italian cook. DeepSeek showed the Chinese industry a viable path to boost model performance while easing the demands on chips.

Those techniques were behind the “DeepSeek shock” in January 2025, when Liang’s company released a powerful but inexpensive new open-source model and U.S. stocks briefly swooned at the unexpected rise of Chinese competition. In a posting on X, OpenAI’s Altman called the DeepSeek model impressive, saying “it’s legit invigorating to have a new competitor!”

Liang’s Chinese rivals were jolted, too. At Moonshot, Yang fielded calls from worried investors, including one who wanted to know why Yang had failed to foresee DeepSeek’s rise, people familiar with the matter said.

Yang incorporated techniques that were invented or proven by DeepSeek into Moonshot’s subsequent models. Its K2 and K3 models adopted the MoE design and variants of MLA. DeepSeek, in turn, used a technique optimized by Moonshot to boost training efficiency and stability.

At a DeepSeek meeting in May, some investors pressed founder Liang about whether he could make much money giving away secrets. His answer captured the ethos of China’s open-source movement. “I’m not worried about competition because the market is big enough,” he said.

In addition to distilling U.S. AI models, some Chinese companies tapped American expertise by hiring Chinese nationals who had worked for U.S. AI leaders. Chinese internet giant Tencent hired two Tsinghua graduates who had been at OpenAI. TikTok parent ByteDance brought back home a Google researcher, and within a year built a powerful video-generation tool that is now widely used by content creators and Hollywood studios.

In January, executives at Tang’s Z.AI—then known as Knowledge Atlas Technology, or Zhipu—struck the gong at the Hong Kong stock exchange, making it the first Chinese AI model startup to go public. By July, Z.AI’s annual recurring revenue—a key metric for subscription-based services—had risen to $1 billion, people familiar with its operations said, about twice that of DeepSeek.

That still trails American titans by a wide margin. Anthropic’s annual revenue reached $65 billion in July, according to investors briefed by the company.

At the DeepSeek meeting in May, Liang said computer chips, not people, accounted for the most critical gap between Chinese and American companies. Researchers at top labs including Alibaba and Z.AI said they are often allotted one-fifth of the high-end chips available to peers at OpenAI and Google.

While Chinese companies pursue “cost-efficiency to the extreme,” said Laila Khawaja, Miami-based analyst at Gavekal Technologies, “U.S. firms with more advanced chips and willingness to burn capital spend a lot more on explorative research that could potentially lead to step-change innovations.”

Several Chinese AI developers are building models with five trillion to 10 trillion parameters, a shorthand figure for a model’s capabilities. People in the industry estimate Anthropic’s latest models have more than five trillion parameters.

Tang is training Z.AI’s next flagship model, which his team hopes will be capable of executing complex research tasks that stretch over weeks, said people familiar with the plans. “Whoever is first to push the technological limit even an inch higher will redefine what’s possible across every industry,” Tang wrote in a memo to employees in July.