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Microsoft CEO Satya Nadella speaks at a company event on artificial intelligence technologies in Jakarta, Indonesia, on April 30, 2024. Microsoft will invest $1.7 billion to build out cloud computing and artificial intelligence infrastructure in Indonesia, betting on Southeast Asia’s biggest economy to spur growth.

Dimas Ardian | Bloomberg | Getty Images

As Microsoft investors get ready for quarterly earnings this month, there’s one particular metric that’s become increasingly important: finance leases.

A finance lease lets a company pay for an asset over years, rather than all upfront. For companies like Microsoft that are building massive data centers to handle artificial intelligence workloads, shareholders have to get used to some big numbers.

In July, Microsoft told investors in a footnote of its annual report that finance leases that had not yet begun had soared to $108.4 billion, up $20.6 billion from the quarter before, and nearly $100 billion higher than two years earlier. Leases will commence between the 2025 and 2030 fiscal years, and will run for up to 20 years, the filing said.

Overall, Microsoft made $19 billion in capital expenditures in the latest quarter. The total, which includes assets acquired under finance leases, was up from $14 billion in the March quarter and was as much as Microsoft shelled out in the entire 2020 fiscal year.

“It’s an insane ramp,” said Charles Fitzgerald, a former Microsoft manager who writes about capital expenditures on his blog Platformonomics.

Investors will get further clarity on Microsoft’s lease finances when the company reports fiscal first-quarter results in late October. Executives at Microsoft and other top tech companies have approved higher capital expenditures in the past two years, often to boost their performance in generative AI.

Last month Microsoft confirmed its participation in a fund to back the development of data centers and the necessary energy infrastructure, mainly in the U.S. It also signed a 20-year power purchase agreement to restart a reactor at the Three Mile Island nuclear plant in Pennsylvania.

Caught off guard

Microsoft’s higher costs in the June quarter weren’t a surprise to those who heeded finance chief Amy Hood’s guidance from April. She said for the third time in a year that Microsoft was expecting capital expenditures to grow “materially.”

Still, RBC Capital Markets’ Rishi Jaluria was caught off guard by the finance lease figure.

“I’m always on the side that capital leases and capital expenditures are going to be way higher than people think, but they exceeded my own expectations,” Jaluria said. “Frankly, I’m trusting Microsoft here.” A capital lease is another term for a finance lease.

Microsoft has said it achieves the best performance and the best cost when it’s building data centers from scratch. But sometimes the company needs additional capacity immediately, and finance leases can help Microsoft obtain it more quickly.

The pace has been frenetic since OpenAI introduced ChatGPT in late 2022. Microsoft supplies computing power to OpenAI, meaning the startup needs enough servers packed with Nvidia graphics processing units to keep ChatGPT online.

With ChatGPT and other OpenAI services becoming even more popular, Microsoft has signed up additional cloud providers, including CoreWeave and Oracle. UBS analysts wrote in a report in September that comments Hood made in January suggest that Microsoft’s finance leases include the relationships with CoreWeave and Oracle.

Microsoft declined to comment on where third-party cloud partnerships show up on its financial statements.

Jaluria said investors don’t pay attention to backlogs for capital leases. Microsoft doesn’t specify when they will kick in or how long they will last, making them less immediate than in-quarter capital expenditures.

CEO Satya Nadella normally defers to Hood when analysts ask financial questions on earnings calls. But in July, Nadella stepped up when an analyst asked about the strategy of forming partnerships with other cloud providers that supplement Microsoft’s direct data center spending.

“To me it’s no different than leases that we’ve already done in the past,” Nadella said. “You could even say sometimes buying from Oracle may be even more efficient leases because they are even shorter date.”

When it comes to the jump in capital expenditures and future finance leases, Jaluria said investors just have to accept that they will weigh on profitability.

“Naturally, margins are coming down,” said Jaluria, who has the equivalent of a buy rating on the stock. “The cost is here now, and the benefits are not here to offset it. And I think that’s OK.”

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Huawei ‘has got China covered’ if the U.S. doesn’t participate, Nvidia CEO tells CNBC

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Huawei 'has got China covered' if the U.S. doesn't participate, Nvidia CEO tells CNBC

If all the AI developers are in China, the China stack is going to win, Nvidia CEO tells CNBC

If the U.S. continues to impose AI semiconductor restrictions on China, then chipmaker Huawei will take advantage of its position in the world’s second-largest economy, Nvidia CEO Jensen Huang told CNBC Thursday.

“Our technology is a generation ahead of theirs,” Huang told CNBC at the sidelines of the Viva Technology conference in Paris.

However, he warned that: “If the United States doesn’t want to partake, participate in China, Huawei has got China covered, and Huawei has got everybody else covered.”

In the face of U.S. export curbs that restrict Chinese firms from buying advanced semiconductors used in the development of AI, Beijing has focused on nurturing domestic firms such as Huawei in a bid to build its own AI chip ecosystem.

Huawei CEO Ren Zhengfei this week told the People’s Daily Newspaper of the governing Communist party that Huawei’s single chip is still behind the U.S. by a generation.

“The United States has exaggerated Huawei’s achievements. Huawei is not that great. We have to work hard to reach their evaluation,” Ren said in comments reported by Reuters.

This is a developing news story and will be updated shortly.

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Nvidia’s first GPU was made in France — Macron wants the country to produce cutting edge chips again

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Nvidia's first GPU was made in France — Macron wants the country to produce cutting edge chips again

Jensen Huang, co-founder and chief executive officer of Nvidia Corp., left, and Emmanuel Macron, France’s president at the 2025 VivaTech conference in Paris, France, on Wednesday, June 11, 2025.

Nathan Laine | Bloomberg | Getty Images

French President Emmanuel Macron on Wednesday made a pitch for his country to manufacture the most advanced chips in the world, in a bid to position itself as a critical tech hub in Europe.

The comments come as European tech companies and countries are reassessing their reliance on foreign technology firms for critical technology and infrastructure.

Chipmaking in particular arose as a topic after Nvidia CEO Jensen Huang, who was doing a panel talk alongside Macron and Mistral AI CEO Arthur Mensch, said on Wednesday that the company’s first graphics processing unit (GPU) was manufactured in France by SGS Thomson Microelectronics, now known as STMicroelectronics.

Yet STMicroelectronics is currently not at the leading edge of semiconductor manufacturing. Most of the chips it makes are for industries like the automotive one, which don’t required the most cutting-edge semiconductors.

Macron nevertheless laid his ambition out for France to be able to manufacture semiconductors in the range of 2 nanometers to 10 nanometers.

“If we want to consolidate our industry, we have now to get more and more of the chips at the right scale,” Macron said on Wednesday.

The smaller the nanometer number, the more transistors that can be fit into a chip, leading to a more powerful semiconductor. Apple’s latest iPhone chips, for instance, are based on 3 nanometer technology.

Very few companies are able to manufacture chips at this level and on a large scale, with Samsung and Nvidia provider Taiwan Semiconductor Manufacturing Co. (TSMC) leading the pack.

If France wants to produce these cutting-edge chips, it will likely need TSMC or Samsung to build a factory locally — something that has been happening in the U.S. TSMC has now committed billions of dollars to build more factories Stateside.

Macron touted a deal between Thales, Radiall and Taiwan’s Foxconn, which are exploring setting up a semiconductor assembly and test facility in France.

“I want to convince them to make the manufacturing in France,” Macron said during VivaTech — one of France’s biggest tech events — on the same day Nvidia’s Huang announced a slew of deals to build more artificial intelligence infrastructure in Europe.

One key partnership announced by Huang is between Nvidia and French AI model firm Mistral to build a so-called “AI cloud.”

France has looked to build out its AI infrastructure and Macron in February said that the country’s AI sector would receive 109 billion euros ($125.6 billion) in private investments in the coming years. Macron touted the Nvidia and Mistral deal as an extension of France’s AI buildout.

“We are deepening them [investments] and we are accelerating. And what Mistral AI and Nvidia announced this morning is a game-changer as well,” Macron told CNBC on Wednesday.

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China’s racing to build its AI chip ecosystem as U.S. curbs bite. Here’s how its supply chain stacks up

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China's racing to build its AI chip ecosystem as U.S. curbs bite. Here's how its supply chain stacks up

Chip engineer handling a wafer.

Sinology | Moment | Getty Images

With the U.S. restricting China from buying advanced semiconductors used in artificial intelligence development, Beijing is placing hopes on domestic alternatives such as Huawei. 

The task has been made more challenging by the fact that U.S. curbs not only inhibit China’s access to the world’s most advanced chips, but also restrict availing technology vital for creating an AI chip ecosystem. 

Those constraints span the entire semiconductor value chain, ranging from design and manufacturing equipment used to produce AI chips to supporting elements such as memory chips. 

Beijing has mobilized tens of billions of dollars to try to fill those gaps, but while it has been able to “brute force” its way into some breakthroughs, it still has a long way to go, according to experts. 

“U.S. export controls on advanced Nvidia AI chips have incentivized China’s industry to develop alternatives, while also making it more difficult for domestic firms to do so,” said Paul Triolo, partner and senior vice president for China at advisory firm DGA-Albright Stonebridge Group.

Here’s how China stacks up against the rest of the world in four key segments needed to build AI chips. 

AI chip design

Nvidia is regarded as the world’s leading AI chip company, but it’s important to understand that it doesn’t actually manufacture the physical chips that are used for AI training and computing.

Rather, the company designs AI chips, or more precisely, graphics processing units. Orders of the company’s patented GPU designs are then sent to chip foundries — manufacturers that specialize in the mass production of other companies’ semiconductor products. 

While American competitors such as AMD and Broadcom offer varying alternatives, GPU designs from Nvidia are widely recognized as the industry standard. The demand for Nvidia chips is so strong that Chinese customers have continued to buy any of the company’s chips they can get their hands on.

But Nvidia is grappling with Washington’s tightening restrictions. The company revealed in April that additional curbs had prevented it from selling its H20 processor to Chinese clients.

Nvidia’s H20 was a less sophisticated version of its H100 processor, designed specifically to skirt previous export controls. Nevertheless, experts say, it was still more advanced than anything available domestically. But China hopes to change that. 

In response to restrictions, more Chinese semiconductor players have been entering the AI processor arena. They’ve included a wide array of upstarts, such as Enflame Technology and Biren Technology, seeking to soak up billions of dollars in GPU demand left by Nvidia.

But no Chinese firm appears closer to providing a true alternative to Nvidia than Huawei’s chip design arm, HiSilicon. 

Huawei’s most advanced GPU in mass production is its Ascend 910B. The next-generation Ascend 910C was reportedly expected to begin mass shipments as early as May, though no updates have emerged. 

Dylan Patel, founder, CEO and chief analyst at SemiAnalysis, told CNBC that while the Ascend chips remain behind Nvidia, they show that Huawei has been making significant progress. 

“Compared to Nvidia’s export-restricted chips, the performance gap between Huawei and the H20 is less than a full generation. Huawei is not far behind the products Nvidia is permitted to sell into China,” Patel said.

He added that the 910B was two years behind Nvidia as of last year, while the Ascend 910C is only a year behind. 

But while that suggests China’s GPU design capabilities have made great strides, design is just one aspect that stands in the way of creating a competitive AI chip ecosystem.

AI chip fabrication

To manufacture its GPUs, Nvidia relies on TSMC, the world’s largest contract chip foundry, which produces most of the world’s advanced chips.

TSMC complies with U.S. chip controls and is also barred from taking any chip orders from companies on the U.S. trade blacklist. Huawei was placed on the list in 2019.

That has led to Chinese chip designers like Huawei to enlist local chip foundries, the largest of which is SMIC.

SMIC is far behind TSMC — it’s officially known to be able to produce 7-nanometer chips, requiring less advance tech than TSMC’s 3-nanometer production. Smaller nanometer sizes lead to greater chip processing power and efficiency.

There are signs that SMIC has made progress. The company is suspected to have been behind a 5-nanometer 5G chip for Huawei’s Mate 60 Pro, which had rocked confidence in U.S. chip controls in 2023.  The company, however, has a long way to go before it can mass-produce advanced GPUs in a cost-efficient manner. 

According to independent chip and technology analyst Ray Wang, SMIC’s known operation capacity is dwarfed by TSMC’s. 

“Huawei is a very good chip design company, but they are still without good domestic chipmakers,” Wang said, noting that Huawei is reportedly working on its own fabrication capabilities. 

But the lack of key manufacturing equipment stands in the way of both companies.

Advanced Chip equipment  

SMIC’s ability to fulfill Huawei’s GPU requirements is limited by the familiar problem of export controls, but in this case, from the Netherlands. 

While Netherlands may not have any prominent semiconductor designers or manufacturers, it’s home to ASML, the world’s leading supplier of advanced chipmaking equipment — machines that use light or electron beams to transfer complex patterns onto silicon wafers, forming the basis of microchips.

In accordance with U.S. export controls, the country has agreed to block the sale of ASML’s most advanced ultraviolet (EUV) lithography machines. The tools are critical to making advanced GPUs at scale and cost-effectively. 

EUV is the most significant barrier for Chinese advanced chip production, according to Jeff Koch, an analyst at SemiAnalysis. “They have most of the other tooling available, but lithography is limiting their ability to scale towards 3nm and below process nodes,”  he told CNBC.

SMIC has found methods to work around lithography restrictions using ASML’s less advanced deep ultraviolet lithography systems, which have seen comparatively fewer restrictions.

Through this “brute forcing,” producing chips at 7 nm is doable, but the yields are not good, and the strategy is likely reaching its limit, Koch said, adding that “at current yields it appears SMIC cannot produce enough domestic accelerators to meet demand.”

SiCarrier Technologies, a Chinese company working on lithography technology, has reportedly been linked to Huawei.

But imitating existing lithography tools could take years, if not decades, to achieve, Koch said. Instead, China is likely to pursue other technologies and different lithography techniques to push innovation rather than imitation, he added.

AI memory components

While GPUs are often identified as the most critical components in AI computing, they’re far from the only ones. In order to operate AI training and computing, GPUs must work alongside memory chips, which are able to store data within a broader “chipset.”

In AI applications, a specific type of memory known as HBM has become the industry standard. South Korea’s SK Hynix has taken the industry lead in HBM. Other companies in the field include Samsung and U.S.-based Micron

“High bandwidth memory at this stage of AI progression has become essential for training and running AI models,” said analyst Wang.

As with the Netherlands, South Korea is cooperating with U.S.-led chip restrictions and began complying with fresh curbs on the sale of certain HBM memory chips to China in December. 

In response, Chinese memory chip maker ChangXin Memory Technologies, or CXMT, in partnership with chip-packaging and testing company Tongfu Microelectronics, is in the early stages of producing HBM, according to a report by Reuters.

According to Wang, CXMT is expected to be three to four years behind global leaders in HBM development, though it faces major roadblocks, including export controls on chipmaking equipment.

SemiAnalysis estimated in April that CXMT remained a year away from ramping any reasonable volume.

Chinese foundry Wuhan Xinxin Semiconductor Manufacturing is reportedly building a factory to produce HBM wafers. A report from SCMP said that Huawei Technologies had partnered with the firm in producing HBM chips, although the companies did not confirm the partnership.

Huawei has leaned on HBM stockpiles from suppliers like Samsung for use in their Ascend 910C AI processor, SemiAnalysis said in an April report, noting that while the chip was designed domestically, it still relies on foreign products obtained prior to or despite restrictions.

“Whether it be HBM from Samsung, wafers from TSMC, or equipment from America, Netherlands, and Japan, there is a big reliance on foreign industry,” SemiAnalysis said.

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