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.
Industrial and infrastructure stocks may soon share the spotlight with the artificial intelligence trade.
According to ETF Action’s Mike Atkins, there’s a bullish setup taking shape due to both policy and consumer trends. His prediction comes during a volatile month for Big Tech and AI stocks.
“You’re seeing kind of the old-school infrastructure, industrial products that have not done as well over the years,” the firm’s founding partner told CNBC’s “ETF Edge” this week. “But there’s a big drive… kind of away from globalization into this reshoring concept, and I think that has legs.”
Global X CEO Ryan O’Connor is also optimistic because the groups support the AI boom. His firm runs the Global X U.S. Infrastructure Development ETF (PAVE), which tracks companies involved in construction and industrial projects.
“Infrastructure is something that’s near and dear to our heart based off of PAVE, which is our largest ETF in the market,” said O’Connor in the same interview. “We think some of these reshoring efforts that you can get through some of these infrastructure places are an interesting one.”
Both ETFs are lower so far this month — but Global X’s infrastructure ETF is performing better. Its top holdings, according to the firm’s website, are Howmet Aerospace, Quanta Services and Parker Hannifin.
“All of the things that are going to be required for us to continue to support this AI boom, the electrification of the U.S. economy, is certainly one of them,” he said, noting the firm’s U.S. Electrification ETF (ZAP) gives investors exposure to them. The ETF is up almost 24% so far this year.
The Global X U.S. Electrification ETF is also performing a few percentage points better than the VanEck Semiconductor ETF for the month.
At ThredUp‘s 600,000-square-foot warehouse in Suwanee, Georgia, roughly 40,000 pieces of used clothing are processed each day. The company’s logistics network — four facilities across the U.S. — now rivals that of some fast-fashion giants.
“This is the largest garment-on-hanger system in the world,” said Justin Pina, ThredUp’s senior director of operations. “We can hold more than 3.5 million items here.”
Secondhand shopping is booming. The global secondhand apparel market is expected to reach $367 billion by 2029, growing almost three times faster than the overall apparel market, according to GlobalData.
About 97 percent of clothing sold in the U.S. is imported, mostly from China, Vietnam, Bangladesh and India, according to the American Apparel and Footwear Association.
“When tariffs raise those costs, resale platforms suddenly look like the smart buy. This isn’t just a fad,” said Jasmine Enberg, co-CEO of Scalable. “Tariffs are accelerating trends that were already reshaping the way Americans shop.”
For James Reinhart, ThredUp’s CEO, the company is already seeing it play out.
“The business is free-cash-flow positive and growing double digits,” said Reinhart. “We feel really good about the economics, gross margins near 80% and operations built entirely within the U.S.”
ThredUp reported that revenue grew 34% year over year in the third quarter. The company also said it acquired more new customers in the quarter than at any other time in its history, with new buyer growth up 54% from the same period last year.
“If tariffs add 20% to 30% to retail prices, that’s a huge advantage for resale,” said Dylan Carden, research analyst at William Blair & Company. “Pre-owned items aren’t subject to those duties, so demand naturally shifts.”
Inside the ThredUp warehouse, where CNBC got a behind-the-scenes look. automation hums alongside human workers. AI systems photograph, categorize, and price thousands of garments per hour. For Reinhart, the technology is key to scaling resale like retail.
“AI has really accelerated adoption,” said Reinhart. “It’s helping us improve discovery, styling, and personalization for buyers.”
That tech wave extends beyond ThredUp. Fashion-tech startups Phia, co-founded by Phoebe Gates and Sophia Kianni, is using AI to scan thousands of listings across retail and resale in seconds.
“The fact that we’ve driven millions in transaction volume shows how big this need is,” Gates said. “People want smarter, cheaper ways to shop.”
ThredUp is betting that domestic infrastructure, automation, and AI will keep it ahead of the curve, and that tariffs meant to revive U.S. manufacturing could end up powering a new kind of American fashion economy.
“The future of fashion will be more sustainable than it is today,” said Reinhart. “And secondhand will be at the center of it.”
CNBC’s Deirdre Bosa asked those at the epicenter of the boom for their take, sitting down with the founders of two of the buzziest AI startups.
Amjad Masad, founder and CEO of AI coding startup Replit, admits there’s been a cooldown.
“Early on in the year, there was the vibe coding hype market, where everyone’s heard about vibe coding. Everyone wanted to go try it. The tools were not as good as they are today. So I think that burnt a lot of people,” Masad said. “So there’s a bit of a vibe coding, I would say, hype slow down, and a lot of companies that were making money are not making as much money.”
Masad added that a lot companies were publishing their annualized recurring revenue figures every week, and “now they’re not.”
Navrina Singh, founder and CEO of startup Credo AI, which helps enterprises with AI oversight and risk management, is seeing more excitement than fear.
“I don’t think we are in a bubble,” she said. “I really believe this is the new reality of the world that we are living in. As we know, AI is going to be and already is our biggest growth driver for businesses. So it just makes sense that there has to be more investment, not only on the capability side, governance side, but energy and infrastructure side as well.”
