Subheadline: AI’s future depends on a semiconductor chain spread across countries, companies and technologies that no single power currently controls.

Standfirst: Nvidia may dominate the AI accelerator market, but the chips behind the AI revolution depend on a far larger ecosystem spanning Taiwan’s foundries, Dutch lithography, advanced memory, packaging, American design software and increasingly contested global supply chains.

The artificial intelligence revolution is often described as a race between models.

That description misses the machinery underneath.

Every major AI system ultimately depends on semiconductors: processors that perform calculations, memory that feeds them data, networking chips that connect thousands of processors, packaging technologies that bring those components together, and manufacturing equipment capable of producing transistors at extraordinary densities.

The competition to control that infrastructure has become one of the defining industrial and geopolitical contests of the 2020s.

And there is no single answer to the question of who controls it.

Nvidia has established an extraordinary position in AI computing. Taiwan dominates advanced semiconductor manufacturing. ASML of the Netherlands controls the world’s most advanced extreme-ultraviolet lithography technology. American companies retain major positions in chip design, electronic-design automation, cloud computing and semiconductor equipment. South Korea is indispensable to advanced memory. China is investing heavily to reduce its dependence on foreign technology.

The result is a supply chain in which power is distributed—but unevenly.

The Semiconductor Industry Association says 100% of the world’s semiconductor manufacturing capacity below 10 nanometers is currently concentrated in Taiwan and South Korea, with Taiwan accounting for 92% and South Korea 8%. Taiwan’s government separately says its companies produce more than 90% of the world’s most advanced semiconductors.

At the same time, ASML says AI-related investment is driving demand for advanced logic and memory and that its customers are accelerating capacity expansion. The company is increasing planned 2027 production capacity for its lower-numerical-aperture EUV systems by 30% from its 2026 capacity of about 65 systems.

The strategic question is therefore becoming more complicated than who makes the best AI chip.

It is becoming:

Who controls the critical layers required to make, connect, supply and scale those chips?

AI begins with a chip—but not just one

The processor inside an AI server is only one component of the computing system.

Modern AI factories require accelerators, CPUs, high-bandwidth memory, networking hardware, storage, optical interconnects, power systems and sophisticated packaging.

Nvidia’s current platforms illustrate how far the industry has moved from the idea of a standalone graphics processor.

The company’s Vera Rubin platform combines GPUs with CPUs, networking chips, data-processing units and switches into rack-scale systems designed to operate as a single AI computing platform. Nvidia says its Rubin systems are intended to support large-scale training and inference, while cloud providers and AI companies deploy them as integrated infrastructure.

That matters because control is increasingly exercised at the system level.

A company may design the central accelerator, but another company may manufacture it. A third may supply the memory. A fourth may provide the lithography machine. Another may provide the software used to design the chip.

The AI semiconductor industry is therefore less like a single race and more like a chain of interdependent bottlenecks.

Control over one bottleneck can influence the entire system.

Nvidia controls an important layer of the stack

Nvidia occupies one of the most strategically important positions because its influence extends beyond the physical GPU.

Its data-center business generated $62.3 billion in quarterly revenue in the fourth quarter of fiscal 2026, while full-year data-center revenue reached $193.7 billion, according to the company’s financial results.

The company’s importance comes from the combination of hardware, networking and software.

Its CUDA software ecosystem has become deeply integrated into AI development, while its GPUs and networking products are deployed in large clusters operated by cloud companies and AI laboratories.

The company’s newer Rubin platform represents another step in that strategy: rather than simply selling processors, Nvidia is increasingly providing an architecture for complete AI factories.

That creates an important distinction.

Nvidia does not control the semiconductor supply chain by itself.

It controls an influential design and computing-platform layer within it.

The physical production of many of those advanced processors depends on other companies.

That dependence leads directly to Taiwan.

Taiwan is the manufacturing center of the AI era

Taiwan Semiconductor Manufacturing Co., or TSMC, operates the world’s most important advanced contract-manufacturing network.

The company’s business model is different from Nvidia’s.

Nvidia primarily designs processors and platforms. TSMC manufactures chips designed by customers.

That separation helped create the modern fabless semiconductor industry, allowing companies to specialize in chip architecture without owning the enormous factories required to produce them.

The model has become especially important for AI.

TSMC says its newest process technologies are being developed specifically to meet rising computing requirements from AI and high-performance computing. Its 2026 technology roadmap includes increasingly advanced process nodes as well as advanced packaging technologies designed to combine more silicon within a single system.

Advanced packaging is becoming increasingly important because simply making transistors smaller is no longer sufficient.

AI processors require enormous quantities of data to move between computing and memory components.

TSMC is therefore expanding its CoWoS advanced-packaging technology, including larger package sizes designed to integrate more silicon for AI and high-performance computing.

This creates a second Taiwanese advantage.

Taiwan is not important only because it manufactures advanced chips.

Its ecosystem combines foundries, packaging, testing, equipment suppliers, component manufacturers and the specialized engineering workforce required to operate them.

Taiwan’s government says the country’s semiconductor industry produces more than 90% of the world’s most advanced chips and that Taiwanese companies produced roughly 90% of global AI servers in 2025.

That concentration is strategically powerful—and strategically vulnerable.

The Taiwan bottleneck

The semiconductor industry’s geographic concentration creates a problem that no individual company can solve.

The SIA estimates that approximately 75% of global semiconductor manufacturing capacity is concentrated in China and East Asia. It identifies more than 50 points in the semiconductor value chain where one region holds more than 65% of the global market.

Advanced production is even more concentrated.

A disruption affecting Taiwan could therefore affect companies that have no physical presence there but depend on Taiwanese manufacturing.

That does not mean every disruption would automatically halt global AI development.

Companies maintain inventories, alternative suppliers exist, and manufacturing is gradually being diversified.

But rebuilding advanced semiconductor capacity is not comparable to constructing an ordinary factory.

It requires specialized equipment, clean rooms, chemical supplies, engineering expertise, process knowledge, supplier networks and years of yield optimization.

That is why governments increasingly view semiconductor manufacturing as strategic infrastructure.

ASML controls another critical bottleneck

If TSMC represents a crucial manufacturing layer, ASML represents an equally important equipment layer.

ASML is the world’s leading supplier of extreme-ultraviolet lithography systems used to manufacture the most advanced semiconductor devices.

EUV machines project extraordinarily precise patterns onto silicon wafers. The technology allows manufacturers to create extremely small structures that would otherwise require more complicated manufacturing techniques.

ASML’s next-generation High-NA EUV systems are now becoming part of the industry’s future production roadmap.

In September 2026, ASML and TSMC announced an initiative to move toward 12-inch photomasks for High-NA EUV, with a pilot line targeted for 2031 and full lithography-system readiness for advanced-node production targeted for 2033. TSMC says it intends to use High-NA technology for high-volume advanced-node manufacturing beginning in 2030.

Samsung has also announced plans to introduce ASML High-NA EUV into future DRAM high-volume manufacturing by 2028.

This illustrates why the semiconductor contest cannot be reduced to a competition between the United States and China.

A Dutch company controls technology that both American and Asian chipmakers need.

The Netherlands therefore occupies a strategic position far larger than the size of its domestic semiconductor market might suggest.

Memory has become a second AI bottleneck

AI chips require more than computational power.

They need memory capable of moving enormous amounts of data quickly.

High-bandwidth memory, or HBM, has consequently become one of the most strategically important components in AI infrastructure.

South Korean companies Samsung Electronics and SK Hynix are major participants in the advanced-memory market, while other suppliers are investing heavily to expand capacity.

The growing importance of memory changes the balance of the semiconductor industry.

For decades, public attention focused primarily on processor performance.

AI has made the connection between processors and memory equally important.

An extremely powerful accelerator can still be constrained if data cannot reach it quickly enough.

That is why semiconductor companies are simultaneously pursuing smaller transistors, larger packages, faster memory and more efficient interconnects.

The AI semiconductor race is becoming a race to optimize the entire computing system.

America controls important parts of the architecture

The United States retains major strengths across the semiconductor value chain.

American companies are prominent in chip design, semiconductor equipment, electronic-design automation, computing architecture and cloud infrastructure.

The Semiconductor Industry Association says U.S.-headquartered semiconductor companies generated $425 billion in semiconductor sales in 2025, representing 53.4% of global market share.

The United States is also trying to expand domestic manufacturing.

The CHIPS and Science Act established incentives for semiconductor manufacturing and research. In July 2026, the Commerce Department announced $874 million in proposed incentives for seven companies working on integrated photonics, compute architectures, advanced packaging, substrates, materials and memory for advanced computing and AI systems.

The strategy is significant because manufacturing capacity alone does not create semiconductor independence.

The United States is investing in the surrounding ecosystem as well.

That includes packaging, photonics, materials, memory and research.

The country’s policy challenge is therefore not simply bringing more factories onto American soil.

It is recreating enough of the supporting industrial ecosystem to make domestic production sustainable.

TSMC is also moving closer to America

The largest example is TSMC itself.

In July 2026, the U.S. Department of Commerce announced that TSMC planned an additional $100 billion investment in the United States, bringing its announced U.S. investment to $265 billion and expanding the planned Arizona manufacturing and packaging footprint to 12 facilities.

That investment does not mean Taiwan’s semiconductor ecosystem is moving wholesale to the United States.

Taiwan’s own government has emphasized the continuing importance of its domestic advanced manufacturing ecosystem. Officials have also said that the country’s most advanced technologies will remain in Taiwan.

Instead, the emerging model is diversification.

Some manufacturing capacity is being built outside Taiwan while Taiwan retains a central role.

That distinction matters.

The objective is not necessarily to replace Taiwan.

It is to reduce the consequences of a disruption in Taiwan.

China is pursuing a different strategy

China’s semiconductor strategy is built around reducing dependence.

U.S. export controls have restricted Chinese access to some advanced processors and semiconductor manufacturing equipment since 2022, with subsequent rules expanding restrictions. The U.S. Government Accountability Office has described the controls as part of Washington’s effort to prevent advanced semiconductor technology from being used in ways that could threaten national security.

The restrictions have affected both finished chips and the machinery used to produce them.

In January 2026, however, the U.S. Commerce Department revised its licensing policy so that applications to export Nvidia H200, AMD MI325X and similar chips to China could be reviewed case by case if specified security requirements are met.

The policy demonstrates the difficulty of using semiconductor controls as a long-term strategy.

Restricting access can slow a competitor’s progress.

But it can also strengthen incentives for that competitor to develop domestic alternatives.

China’s government has explicitly made semiconductor self-reliance a strategic objective. Its 2026–2030 planning documents call for breakthroughs across the full chain of integrated circuits and other core technologies.

A separate 2026 AI action plan calls for secure and reliable supplies of key AI technologies and specifically emphasizes coordinated development of AI-chip hardware and software.

The objective is not simply to produce a Chinese equivalent of one foreign GPU.

It is to build a domestic ecosystem.

Export controls have become industrial policy

This is where semiconductors become geopolitics.

The United States is attempting to restrict China’s access to some advanced technology while simultaneously expanding domestic production.

China is attempting to develop alternatives while protecting its own supply chains and expanding domestic capabilities.

Europe is attempting to reduce strategic dependencies.

Taiwan is strengthening its domestic ecosystem while expanding investment abroad.

Japan and South Korea are protecting their own positions while remaining integrated into the broader allied semiconductor network.

The Netherlands is doing the same with lithography.

The result is not a clean division into two independent semiconductor blocs.

It is a process of selective decoupling.

Some technologies are being restricted for security reasons. Others continue to cross borders because the industry remains economically interdependent.

Europe does not want to become a spectator

The European Union has recognized that its dependence on external suppliers creates a strategic vulnerability.

In June 2026, the European Commission proposed a Chips Act 2.0 aimed at strengthening Europe’s semiconductor industry, reducing strategic dependencies and supporting advanced chip production.

The Commission says the global semiconductor market could reach €1.37 trillion by 2030 and estimates that AI-related components could account for about 70% of the market’s growth.

Europe has strengths of its own.

ASML is the most obvious example.

The continent also has important semiconductor equipment, research, materials and industrial companies.

But Europe remains dependent on foreign suppliers in important parts of advanced chip manufacturing and design.

Its strategy is therefore less about achieving complete self-sufficiency and more about ensuring that it cannot be excluded from strategically important parts of the value chain.

No country can easily become semiconductor-independent

There is an important economic reason why complete national self-sufficiency remains difficult.

The semiconductor industry was built around specialization.

Designers became separate from manufacturers. Manufacturers specialized in particular process technologies. Equipment companies developed highly specialized machinery. Memory producers developed different expertise from logic-chip manufacturers. Packaging became increasingly sophisticated.

The result is an ecosystem in which specialization drives efficiency.

The SIA estimates that rebuilding fully self-sufficient regional semiconductor supply chains would require at least $1 trillion in additional upfront investment and could raise semiconductor prices by 35% to 65%.

That does not mean governments will abandon efforts to diversify.

It means the emerging objective is more likely to be strategic resilience than absolute independence.

A country may decide that it does not need to manufacture every component domestically.

It may instead need enough domestic or allied capacity to survive a disruption.

The next battlefield is advanced packaging

One of the least understood parts of the AI hardware race is packaging.

For traditional chips, packaging was often treated as a final manufacturing step.

For modern AI systems, it has become part of the computing architecture.

Large AI accelerators increasingly combine multiple components in tightly integrated packages with high-bandwidth memory.

This allows computing resources and memory to communicate more efficiently.

TSMC’s expansion of CoWoS demonstrates the strategic importance of this transition. The company says it is producing increasingly large CoWoS packages and developing larger versions to accommodate the computing and memory requirements of AI.

The United States is now investing in packaging as part of its semiconductor strategy.

The Commerce Department’s 2026 R&D incentives include projects involving advanced packaging, photonics, substrates and memory.

That is an indication of where the competition is heading.

The future advantage may not belong simply to whoever can make the smallest transistor.

It may belong to whoever can assemble the most powerful and efficient computing system from multiple components.

The AI supply chain is becoming a political supply chain

The semiconductor industry once operated primarily according to commercial efficiency.

Geopolitics has changed that calculation.

Companies now have to consider export restrictions, national-security reviews, government incentives, regional production requirements and the possibility of sudden supply disruption.

Governments, meanwhile, have discovered that semiconductor technology provides leverage.

Control over advanced processors can restrict another country’s AI development.

Control over lithography equipment can restrict its ability to manufacture advanced chips.

Control over memory can constrain AI-system scaling.

Control over manufacturing capacity can influence which companies receive the newest processors.

And control over software ecosystems can determine which hardware developers choose to build around.

Power is distributed across all these layers.

Who actually controls the AI chip supply chain?

The answer depends on which part of the chain is being examined.

Nvidia has major influence over AI accelerators, system architecture and the software ecosystem surrounding them. Its Rubin platform shows how that influence is expanding toward complete AI infrastructure.

TSMC has extraordinary influence over leading-edge manufacturing and advanced packaging, making Taiwan central to the production of many advanced AI processors.

ASML controls an indispensable technology layer in advanced lithography, with its EUV systems forming part of the manufacturing path for leading-edge chips.

Samsung and SK Hynix are central to advanced memory, an increasingly critical component of AI infrastructure.

The United States retains major positions in chip architecture, design, equipment, software and cloud computing while using export controls and industrial policy to influence the global technology environment.

China possesses enormous semiconductor demand and a rapidly expanding domestic ecosystem, while its government is explicitly targeting greater self-reliance.

Europe, Japan and South Korea occupy strategically important positions in equipment, materials, memory, manufacturing and research.

No single player controls all of it.

That may be the most important fact about the AI semiconductor race.

The next decade will be about resilience as much as speed

The AI industry is still operating in a period of extraordinary semiconductor demand.

SIA says global semiconductor sales reached $795.6 billion in 2025 and that WSTS projects global sales of $1.5 trillion in 2026. The association estimates that AI infrastructure could account for a substantial share of future semiconductor investment.

At the same time, semiconductor manufacturers are investing in future generations of process technology.

TSMC is planning future nodes beyond its current leading-edge roadmap. ASML and its customers are preparing for High-NA EUV. Samsung is preparing to apply High-NA technology to advanced memory. The United States is investing in packaging and compute research. China is pursuing domestic alternatives.

The industry is therefore expanding while simultaneously fragmenting.

That combination creates a paradox.

The world needs more semiconductor capacity because AI demand is growing.

But geopolitical competition is making it harder to maintain one fully integrated global production system.

The real contest is over the bottlenecks

The future of AI will not be determined by one company winning a conventional race.

It will depend on which countries and companies can secure enough access to the bottlenecks that cannot easily be replaced.

Advanced manufacturing is one.

Lithography is another.

High-bandwidth memory is another.

Advanced packaging is increasingly important.

So are chip-design tools, networking technology, specialized materials and the engineering talent required to operate the entire system.

This means that the phrase “AI chip race” can be misleading.

There is no single race.

There is a network of races occurring simultaneously.

Nvidia competes to define the computing architecture.

TSMC competes to maintain manufacturing leadership.

ASML advances the equipment needed to shrink transistors.

Samsung and SK Hynix compete in advanced memory.

China seeks to close technological gaps through domestic development.

The United States and its partners seek to maintain technological advantages while reducing supply-chain vulnerabilities.

Europe seeks greater strategic autonomy without abandoning international specialization.

Taiwan seeks to remain indispensable while reducing the risks associated with excessive geographic concentration.

Conclusion

The AI revolution may look digital and weightless from the outside.

Its foundation is physical.

It is built in semiconductor factories, lithography laboratories, memory plants, packaging facilities and enormous data centers.

And those facilities are concentrated in a surprisingly small number of places.

That concentration gives individual companies and countries extraordinary influence.

But it also means no single actor can completely control the AI semiconductor system.

Nvidia cannot manufacture every chip it designs. TSMC cannot produce chips without equipment from companies such as ASML. Advanced processors cannot deliver their full performance without memory and packaging. The United States cannot manufacture the entire semiconductor ecosystem domestically overnight. China cannot simply replace every foreign technology at once.

The strategic contest is therefore moving toward control of critical dependencies.

Who can make the most advanced chips?

Who can manufacture them at scale?

Who controls the machines required to manufacture them?

Who supplies the memory?

Who owns the design software?

Who can package billions of transistors and memory cells into a functioning AI system?

And who can maintain access to all of those components when geopolitics interrupts normal trade?

Those questions will help determine how quickly AI advances, where its infrastructure is built and which governments have leverage over its development.

The companies building the AI models may receive the public attention.

The companies and countries controlling the silicon underneath them may hold much of the strategic power.

Reporting Credit: Taiwan Semiconductor Manufacturing Company (TSMC), for its 2026 advanced-process and advanced-packaging roadmap; ASML, for EUV and High-NA EUV technology developments and customer-capacity data; NVIDIA, for its 2026 AI-computing platform and financial disclosures; U.S. Department of Commerce and Bureau of Industry and Security, for semiconductor export-control policies and CHIPS-related investments; U.S. Government Accountability Office, for assessment of semiconductor export controls; U.S. National Institute of Standards and Technology, for U.S. semiconductor R&D and manufacturing investment records; Taiwan’s Ministry of Economic Affairs and Executive Yuan, for Taiwan’s semiconductor-industry and investment data; China’s State Council and government ministries, for official semiconductor self-reliance and AI-development policies; European Commission, for the EU Chips Act 2.0 and semiconductor strategy; Semiconductor Industry Association, for global semiconductor-market and supply-chain data; Brown University’s Costs of War Project is not used for this article’s core reporting.

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At its Huawei Connect conference in Shanghai on Sept. 17, the Chinese technology company introduced the Atlas 960E SuperPoD and upgraded its AI computing architecture. Huawei also brought forward the timetable for its next-generation Ascend 960 chips, with the Ascend 960DT now scheduled for the first quarter of 2027 and the Ascend 960PR for the third quarter.

The announcements demonstrate how quickly Huawei is expanding beyond individual processors into large-scale computing systems designed to link thousands of AI accelerators.

Huawei is building beyond the chip itself

The Atlas 960E SuperPoD is designed around Huawei’s Ascend 960 processors and uses near-packaged optics, or NPO, for high-speed connections between components.

Huawei says the system can connect up to 4,096 AI processors. Its broader architecture, called Peerium, is intended eventually to connect much larger numbers of processors into a single computing system. Huawei says its long-term architecture could scale to as many as 1 million processors.

That reflects a central challenge in modern AI computing: performance increasingly depends not only on the capabilities of individual accelerators but also on how efficiently large numbers of processors, memory, storage and networking components work together.

Huawei said its Atlas 960 SuperPoD can improve training and inference performance for models with as many as 10 trillion parameters compared with the preceding Atlas 950 system. Those figures are company claims rather than independent benchmarks.

Ascend roadmap moves ahead

Huawei also said development of the Ascend 960 had progressed faster than expected.

The company now plans to release the Ascend 960DT in the first quarter of 2027 and the Ascend 960PR in the third quarter. The DT version is intended primarily for AI-model training, while the PR version is designed for inference, when trained models process requests and generate results.

Huawei also announced plans for the Ascend 970 in 2028 and Ascend 980 in 2029. The company said each generation is expected to improve computing performance as well as memory and interconnect capabilities.

The accelerated schedule comes as Chinese technology companies increasingly seek domestic alternatives to Nvidia’s AI processors.

Nvidia still has a major software advantage

Huawei’s hardware progress does not by itself mean that the company has matched Nvidia across the AI computing market.

Nvidia has built a substantial software ecosystem around CUDA, its widely used platform for programming and running applications on its processors. That software base is an important part of Nvidia’s position in AI computing and is difficult to reproduce simply by improving chip performance.

Huawei is attempting to address that problem through its own software ecosystem. The company said Ascend now supports more than 90 major open-source projects and that its computing platform has been used to train more than 40 AI models.

Huawei also said more than 5,200 developers were active each month on Ascend-related software.

Those figures come from Huawei and have not been independently audited.

Chinese demand is already exceeding Huawei’s capacity

Huawei’s challenge is not simply a question of technological development. The company is also struggling to produce enough AI computing equipment to meet demand inside China.

Rotating chairman Eric Xu said Sept. 17 that Huawei did not have sufficient capacity to satisfy Chinese demand and therefore was not planning a full-scale international expansion of its AI computing products. He said overseas supplies would remain limited.

Huawei has already deployed more than 1,000 systems based on its earlier Ascend 910C processors, according to company materials. Its newer Ascend 950 systems have also entered commercial use.

The supply constraint indicates that China’s effort to build a domestic AI-chip ecosystem is generating substantial demand, while also exposing bottlenecks in the production chain.

Advanced memory remains a constraint

High-bandwidth memory, or HBM, is one of those constraints.

HBM allows AI processors to move large quantities of data rapidly and has become a critical component of high-performance AI accelerators. U.S. export controls have restricted China’s access to some advanced HBM products, while Chinese chipmakers have faced higher costs and tighter supplies.

Reuters reported earlier in September that Huawei and other Chinese AI-chip companies had raised prices for current and upcoming processors amid higher HBM costs. Sources cited by the agency said Huawei’s Ascend 950DT was being offered at more than 250,000 yuan, although the company has not publicly confirmed those reported prices.

That creates a second challenge for China’s domestic AI industry: developing competitive processors is only one part of building a large-scale supply chain.

U.S. controls remain part of the technology race

The acceleration of Huawei’s chip program is closely tied to U.S. restrictions on advanced computing technology.

The U.S. Commerce Department’s Bureau of Industry and Security has imposed controls affecting advanced computing chips, semiconductor manufacturing equipment and high-bandwidth memory connected to China. In 2025, BIS specifically warned about certain Chinese advanced-computing integrated circuits, including Huawei’s Ascend 910B, 910C and 910D.

U.S. policy has also changed over time. In January 2026, BIS said applications to export Nvidia H200 and AMD MI325X chips to China would be reviewed case by case under specified security conditions.

The result is a technology market in which Chinese companies are simultaneously developing domestic alternatives while navigating restrictions on access to some foreign hardware, manufacturing technology and advanced memory.

China is building an alternative AI computing stack

Huawei’s latest announcements therefore extend beyond a new processor generation.

The company is attempting to build an integrated computing system encompassing AI accelerators, interconnects, optical networking, memory, storage and software. Its stated objective is to make large numbers of processors operate together efficiently rather than relying on a single high-performance chip.

China’s domestic AI industry is also not dependent on Huawei alone. Companies including Cambricon, MetaX and Iluvatar CoreX are developing or supplying alternative AI accelerators, while Chinese technology companies are increasing efforts to develop domestic computing capabilities.

The market remains constrained by manufacturing capacity, advanced memory availability and software compatibility.

For Huawei, the immediate milestone is the speed of its development. The company moved the Ascend 960DT launch forward by several months and is already planning subsequent generations through 2029.

That does not establish that Huawei has overtaken Nvidia. It does show that China’s leading chip companies are moving rapidly to narrow dependence on Nvidia’s technology and to build a domestic AI computing infrastructure capable of operating at increasingly large scale.

 Reporting Credit: Huawei Technologies — Ascend 960 roadmap, Atlas SuperPoD architecture, UnifiedBus and Peerium computing systems; U.S. Department of Commerce, Bureau of Industry and Security — semiconductor, advanced-computing and HBM export-control framework.

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Apple unveiled the device on Sept. 9 alongside its iPhone 18 Pro lineup, making the foldable model the centerpiece of the company’s latest product cycle. The launch also marked new Chief Executive John Ternus’ first major iPhone event after succeeding Tim Cook.

The iPhone Duo starts at $1,999 in the United States, making it Apple’s most expensive iPhone. Preorders begin Oct. 16, with sales scheduled to start Oct. 23.

Apple enters a market shaped by rivals

The Duo arrives more than seven years after Samsung introduced its first Galaxy Fold, while Chinese manufacturers including Huawei have expanded the range of foldable devices available to consumers.

Apple is betting that its combination of hardware, software and services can make foldables attractive to a much broader audience. Analysts cited by Reuters expect the company’s entry to significantly increase the size of the market and potentially give Apple a large share of foldable-phone revenue.

Apple has emphasized that the Duo is designed not simply as a smaller tablet but as an iPhone that can change how users interact with applications.

When closed, the device has a 5.4-inch outer display. Opening it reveals a 7.6-inch inner display, Apple’s largest iPhone screen to date. The two displays use the same aspect ratio, allowing content to transition between them while supporting multitasking and different viewing positions.

Hardware is built around the foldable design

The Duo uses Apple’s A20 Pro chip and a custom vapor-chamber thermal system designed to sustain performance during gaming, multitasking and on-device artificial-intelligence workloads.

Apple says the chip uses a 2-nanometer manufacturing process and provides faster CPU and GPU performance than the previous generation, while its neural-engine improvements are intended to support more demanding AI processing directly on the phone.

The phone also uses a dual-battery architecture, with batteries positioned on either side of the folding device. Apple says the Duo can provide up to 24 hours of usage when both displays are used equally and can charge to 50% in about 20 minutes.

The device has a titanium frame and hinge cover and supports storage capacities ranging from 256GB to 2TB. It is also eSIM-only worldwide, according to Apple.

Apple emphasizes productivity and AI

The larger internal screen allows users to run applications side by side and use the device in orientations that are not available on conventional iPhones.

Apple demonstrated the Duo with productivity applications, video calls and multitasking features. The company is also integrating its latest AI capabilities into the device, including an updated Siri and Apple Intelligence functions designed to perform as much processing as possible on the phone itself.

Apple says tasks requiring additional computing power can use its Private Cloud Compute system, which it presents as a way to extend AI capabilities while maintaining privacy protections.

The Duo also introduces foldable-specific camera features. Its 48-megapixel dual-camera system can use the outer display as a preview screen for photographs, while the device’s folding form allows users to position the phone for hands-free photography and video calls.

Price could limit the market

The biggest obstacle may be cost.

At $1,999, the Duo is positioned well above Apple’s mainstream iPhone models and roughly at the premium end of the foldable market. Reuters reported that the starting price is about $100 higher than comparable Samsung and Google foldable models.

That price places Apple in a market that remains relatively small compared with conventional smartphones. Foldables account for less than 5% of global smartphone sales, according to figures cited by the Associated Press.

Apple therefore needs to persuade existing iPhone users that the additional cost provides enough practical value to justify moving to a fundamentally different form factor.

The company is also entering the market after competitors have already gained experience with hinge durability, display technology, multitasking software and consumer expectations around foldable phones.

Apple raises the stakes for smartphone makers

The launch nevertheless changes the competitive landscape.

Samsung has spent years building its Galaxy Z Fold and Z Flip lines, while Chinese companies have pushed foldable hardware toward thinner designs and increasingly sophisticated displays. Apple’s arrival gives the category access to one of the world’s largest smartphone ecosystems and could pressure competitors to accelerate their own hardware and software development.

The broader iPhone launch also reflects Apple’s attempt to increase the value of its premium devices as smartphone growth matures.

The company raised the starting prices of the iPhone 18 Pro and Pro Max by $100, to $1,199 and $1,299 respectively, while adding upgraded cameras and the A20 Pro chip.

The strategy places Apple’s latest products at progressively higher price points while emphasizing AI, performance and new device formats as reasons for customers to upgrade.

The iPhone Duo’s success will ultimately depend on whether consumers see the foldable design as more than a novelty. If Apple can turn the form factor into a mainstream iPhone experience, its entry could accelerate adoption across the smartphone industry. If demand remains concentrated among premium buyers, foldables may continue to occupy a specialized segment despite Apple’s arrival.

Reporting Credit: Apple — product specifications, pricing, availability and technology features; — market context and competitive landscape;  — foldable smartphone market context.

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The trial, which began this week after nearly eight years of legal proceedings, places one of China’s most important technology companies at the center of a major U.S. prosecution with implications for intellectual property, export controls and Washington’s broader technology conflict with Beijing.

U.S. prosecutors told jurors in opening statements that Huawei engaged in a pattern of criminal conduct spanning roughly two decades. They allege the company and related entities sought confidential technology from American competitors and used the resulting information to strengthen Huawei’s global telecommunications business.

The government’s case includes allegations involving Cisco Systems, T-Mobile and other U.S. technology companies. Prosecutors say the allegedly misappropriated material included router source code, cellular technology and robotics-related technology.

Prosecutors portray a long-running corporate strategy

The case is built around a superseding indictment filed in 2020 that charged Huawei and several subsidiaries with racketeering conspiracy and conspiracy to steal trade secrets, alongside fraud, sanctions and obstruction-related offenses.

According to the Justice Department, Huawei allegedly used a range of methods to obtain proprietary technology, including recruiting employees from competitors, violating confidentiality agreements and using intermediaries to obtain information. Prosecutors also alleged that Huawei established incentives for employees who obtained valuable confidential material from competitors.

The government further alleges that Huawei and affiliated entities conducted business connected to countries under U.S. sanctions, including Iran and North Korea, while concealing aspects of those activities from financial institutions and U.S. authorities.

One major component involves Skycom, a company that U.S. prosecutors alleged was controlled by Huawei and used in connection with its Iranian operations. The government has said Huawei misrepresented its relationship with Skycom and the nature of its Iranian business to financial institutions.

The racketeering allegations encompass conduct that prosecutors say generated or supported commercial benefits for Huawei. The indictment alleges that proceeds associated with the alleged activity were reinvested into the company’s wider operations.

Huawei rejects the government’s characterization

Huawei’s defense has presented the case very differently.

Defense lawyers told jurors that the company’s growth resulted from engineering, investment and competition rather than a coordinated criminal enterprise. They argued that prosecutors are presenting isolated actions by individual employees as evidence of a companywide conspiracy.

The defense has also challenged the government’s reliance on incidents involving rival technology companies, arguing that employees responsible for improper conduct were disciplined and that such episodes do not establish a corporate policy to steal technology.

Huawei has previously denied the allegations and maintained that innovation and respect for intellectual property were central to its business. Its lawyers have also challenged the breadth of the U.S. prosecution and sought to exclude evidence gathered during the government’s investigation. A federal judge rejected a Huawei motion to suppress several pieces of evidence in June 2026.

The Iran allegations remain central to the case

The prosecution also reaches beyond intellectual property.

In 2021, Huawei Chief Financial Officer Meng Wanzhou entered a deferred prosecution agreement with U.S. authorities and admitted to making material misrepresentations to a financial institution concerning Huawei’s operations in Iran. The agreement resolved the criminal charges against Meng while allowing the government’s case against Huawei to continue.

The U.S. government alleged that Huawei concealed its relationship with Skycom and that transactions connected to Huawei’s Iranian business passed through the U.S. financial system.

Meng’s admissions are expected to form part of the evidence presented against Huawei, although she is not a defendant at the current trial.

The original case also became a major diplomatic dispute after Meng was arrested in Canada in 2018 on a U.S. extradition request. She returned to China in 2021 after the deferred prosecution agreement, while the United States withdrew its extradition request.

A case with broader technology implications

The Huawei prosecution comes as Washington and Beijing remain locked in a wider contest over advanced technology, trade and national security.

Huawei has been subjected to extensive U.S. export restrictions, limiting its access to American technology and components. Those restrictions have contributed to the company’s shift toward domestic semiconductor development and other technologies, including chips used in artificial intelligence applications.

The trial therefore goes beyond the individual allegations against Huawei. A conviction could reinforce the U.S. government’s broader argument that technology theft, sanctions evasion and the misuse of international financial systems represent interconnected national-security risks.

At the same time, the defense’s success in persuading jurors that the prosecution has assembled unrelated incidents into a single conspiracy could have significant implications for the government’s ability to pursue similarly broad cases against multinational technology companies.

The trial is expected to last about three months. Huawei remains presumed innocent unless prosecutors prove the charges beyond a reasonable doubt.

Reporting Credit: U.S. Department of Justice, Eastern District of New York — criminal charges, superseding indictment and prosecution history; U.S. District Court for the Eastern District of New York – current trial proceedings and courtroom developments.

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Huawei unveiled the Mate XT2, its latest tri-fold smartphone, while Xiaomi introduced the Xiaomi 18 Fold on September 7. Huawei is continuing with a two-hinge design intended to expand a smartphone into a substantially larger display, whereas Xiaomi has chosen a conventional book-style foldable architecture.

The launches add another layer to competition in China’s premium smartphone market, where manufacturers are using increasingly differentiated hardware and internally developed technologies to compete for high-end consumers.

Huawei Extends Its Tri-Fold Architecture

Huawei’s Mate XT2 builds on the company’s existing tri-fold approach, using two folding mechanisms to expand the device into a larger-screen format.

The previous Mate XT featured a fully opened display measuring 10.2 inches diagonally. Huawei’s new generation retains the basic architecture while introducing updated hardware and structural changes.

The Mate XT2 is powered by Huawei’s Kirin 9050 Pro processor and uses a LogicFolding architecture that the company says is designed to improve performance and efficiency. Huawei has also emphasized durability and privacy features as part of its effort to develop the more complex folding format for everyday use.

Launch information places the Mate XT2 at 19,999 yuan for the entry configuration, rising to 24,999 yuan for the highest configuration. Sales are scheduled to begin September 12.

The pricing puts the device firmly into the luxury segment and distinguishes Huawei’s tri-fold strategy from more conventionally priced premium smartphones.

Xiaomi Chooses the Book-Style Foldable

Xiaomi is taking a different route with the Xiaomi 18 Fold, which opens from a phone-sized exterior into a larger internal display.

The company has positioned the model as a premium flagship and highlighted its internally developed XRING O3 processor. Xiaomi’s official launch material describes the 18 Fold as its first flagship smartphone powered by the next-generation chip.

The device also features a Leica-branded camera system, extending Xiaomi’s use of imaging technology as a component of its premium smartphone strategy.

Pricing starts at 10,999 yuan and reaches 14,999 yuan for the highest configuration. That makes the Xiaomi considerably less expensive than Huawei’s Mate XT2, although both remain positioned above conventional flagship smartphones.

The difference in architecture and pricing gives the companies distinct propositions within the same premium foldable market.

Proprietary Technology Becomes Part of the Competition

The launches highlight a broader shift in how China’s leading smartphone manufacturers are approaching flagship hardware.

Huawei has developed alternatives to technologies and components affected by U.S. restrictions, while Xiaomi has invested in proprietary semiconductor technology of its own. As a result, processors and other internally developed technologies increasingly form part of broader product strategies rather than serving simply as individual hardware components.

That approach can give manufacturers greater control over product development while reducing dependence on external suppliers. It also makes the smartphone itself a showcase for capabilities spanning semiconductors, software, displays, imaging and artificial intelligence.

The two foldables therefore represent more than competing form factors. They also illustrate how Chinese smartphone companies are integrating internally developed technologies into high-end consumer devices.

Foldable Design Becomes a Premium Differentiator

Foldable smartphones remain a smaller segment of the overall mobile-phone market, but the category has become an important area for differentiation among manufacturers competing at the premium end.

The underlying proposition is to provide a substantially larger display without requiring users to carry a conventional tablet-sized device. For manufacturers, folding hardware also creates opportunities to distinguish flagship products as conventional smartphone designs become increasingly mature.

Huawei’s continued investment in a tri-fold configuration indicates an effort to move beyond the two-panel designs more commonly associated with foldable phones.

Xiaomi’s 18 Fold takes a less complicated structural approach while competing through other premium technologies, including its processor platform and Leica-branded imaging system.

A Wide Price Gap Reflects Different Strategies

The two launches also demonstrate how differently manufacturers are positioning foldable technology.

Huawei’s Mate XT2 starts at nearly 20,000 yuan, while Xiaomi’s 18 Fold begins at 10,999 yuan. The gap gives Xiaomi a lower-priced entry point into the premium foldable segment, while Huawei is positioning its tri-fold architecture as a significantly more expensive technology proposition.

A central question for Huawei’s approach is whether consumers will consider the additional folding section and resulting display flexibility worth the substantial price premium.

The answer will depend on practical factors including durability, software support, battery life and everyday usability, as well as the perceived value of the larger display.

Competition Extends Beyond China’s Domestic Market

The launches come during a busy period for the global smartphone industry. Apple is expected to unveil new products on September 9, immediately following the Huawei and Xiaomi announcements.

For Chinese manufacturers, competition therefore extends beyond the domestic market and includes global companies with established brands and large technology ecosystems.

The increasingly differentiated hardware strategies of Huawei and Xiaomi are part of that wider competition. Their latest foldables show how premium smartphone manufacturers can pursue different combinations of industrial design, semiconductor development, imaging and display technology.

Domestic Semiconductor Development Gains Importance

The Xiaomi 18 Fold’s use of the XRING O3 also highlights the growing role of domestic semiconductor development in China’s consumer electronics sector.

Xiaomi’s investment in its own processor technology gives the company another internally controlled component for its flagship devices. Huawei, meanwhile, continues developing proprietary hardware technologies while operating under restrictions that affect its access to some advanced foreign components.

The implications extend beyond smartphones. Greater reliance on domestically developed processors and other technologies connects consumer electronics competition with China’s broader development of semiconductor, artificial-intelligence and advanced manufacturing capabilities.

For the smartphone industry, the result is a competitive environment in which the architecture of the device and the technologies inside it are increasingly linked to wider corporate technology strategies.

Two Paths for the Next Foldable Generation

Huawei and Xiaomi are entering the latest phase of the Chinese foldable market with markedly different designs.

Huawei is using a tri-fold architecture to pursue a larger display within a smartphone-oriented form factor. Xiaomi is retaining the more established book-style configuration while emphasizing its own processor technology and premium imaging system.

Their launches leave consumers with two different approaches to high-end foldable hardware—and manufacturers with a continuing test of how much complexity, cost and proprietary technology the market is willing to support.

For China’s smartphone industry, the significance extends beyond the two devices. The launches show that competition at the premium end is increasingly being fought through proprietary semiconductor technology, industrial design and integrated hardware ecosystems as well as conventional smartphone specifications.

Reporting Credit: Huawei — official Mate XT2 product and launch information; Xiaomi — official September 7, 2026 announcement for the Xiaomi 18 Fold and XRING O3 platform; Xiaomi Community — official Xiaomi 18 Fold product information, including the Leica camera configuration; Huawei and Xiaomi — official corporate and product materials concerning their respective proprietary technologies.

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The shift underscores a broader strategic realignment in the global AI industry, where access to advanced computing hardware has become central to technological competitiveness. While Nvidia remains the world’s leading AI chip designer, its ability to compete in China has been constrained by U.S. export controls, creating opportunities for Chinese manufacturers to capture market share.

Nvidia Chief Executive Jensen Huang acknowledged the company’s changing position during a recent interview with The Associated Press, saying the company once controlled approximately 95% of China’s AI chip market before U.S. export restrictions significantly limited its business.

“We have to have, number one, make sure that we have national security and that we protect our nation, but we also simultaneously should go and compete and grow our technology industry and maximize our exports,” Huang said.

His comments came after a visit to Beijing in May, where he attracted significant public attention while the United States and China continued discussions on trade and technology issues. Although U.S. President Donald Trump later agreed to permit sales of Nvidia’s H200 AI chips, Chinese policymakers had already intensified efforts to promote domestically designed alternatives.

Huawei emerges as China’s leading AI chip competitor

Since Washington imposed restrictions beginning in 2019 on Huawei and later expanded controls covering China’s access to advanced semiconductors and chipmaking equipment, Chinese companies have accelerated investments in domestic semiconductor development.

Huawei has emerged as the leading beneficiary of that policy shift.

According to research from Bernstein, Nvidia held roughly 40% of China’s AI chip market during 2025, about equal to Huawei’s share. The research firm projects Huawei’s market share could climb to around 50% this year, while Nvidia’s could decline to approximately 8%.

Antonia Hmaidi, a semiconductor specialist at the Mercator Institute for China Studies, said Huawei has become the dominant domestic supplier.

“Nvidia has definitely lost significant ground to Huawei, which now leads domestically,” she said.

Industry analysts say Huawei’s latest commercial AI processors in its Ascend 950 series are broadly comparable in some respects to Nvidia’s H200 chips, although Nvidia continues to lead in cutting-edge AI hardware.

He Hui, director of semiconductor research at Omdia, said China’s confidence in domestic chip development has grown significantly.

“China now believes in its own self-sufficiency and supply capabilities,” He said.

Huawei has also expanded its AI computing infrastructure by deploying large computing clusters powered by thousands of its own processors despite relying on domestically manufactured semiconductors because of continuing export restrictions.

Asked recently how Huawei’s chip technology compares with international competitors, He Tingbo, who heads Huawei’s semiconductor business, said the company has found “pretty good solutions.”

“Who can walk faster? Huawei or other companies? I don’t know the answer,” she said. “I think only time will tell.”

Nvidia remains critical for advanced AI development

Despite Huawei’s rapid progress, analysts say Nvidia’s technology continues to play a vital role in China’s AI ecosystem.

The semiconductor industry remains deeply interconnected globally. Nvidia designs its processors using technology that depends on advanced extreme ultraviolet lithography machines produced by Dutch company ASML, while Taiwan Semiconductor Manufacturing Co. manufactures many of Nvidia’s most advanced chips.

China remains prohibited from purchasing Nvidia’s highest-end AI processors as well as ASML’s most advanced chipmaking equipment under U.S. export controls.

Industry observers note that Huawei’s latest processors still trail Nvidia’s most advanced offerings in several technical areas, particularly for training large AI models.

Chinese AI developers, including DeepSeek, continue to rely on Nvidia hardware for some advanced workloads, while universities and major technology companies also seek access to chips such as the H200 for research and development.

Recent cases involving the alleged smuggling of Nvidia AI chips into China further illustrate continuing demand for the company’s technology despite export restrictions.

AI demand continues to drive Nvidia’s global growth

Although China has become a more difficult market, Nvidia’s worldwide business continues to expand rapidly as investment in artificial intelligence infrastructure accelerates.

The company expects revenue of approximately $91 billion during its May-to-July quarter, excluding any China data-center computing revenue, compared with nearly $82 billion in the previous quarter.

Nvidia generated nearly $216 billion in annual revenue during its latest fiscal year, while Huawei reported approximately $126 billion in revenue over a comparable period.

Nvidia developed its H20 processor specifically to comply with U.S. export restrictions by reducing computing performance. However, shipments into China have gradually declined, according to Brady Wang, a semiconductor analyst at Counterpoint Research.

Huang also told shareholders recently that Nvidia has not yet generated revenue from H200 chip sales in China and remains uncertain whether imports will ultimately receive approval.

Huawei expands long-term global ambitions

Huawei’s ambitions extend beyond China’s domestic market.

Already the world’s largest supplier of telecommunications networking equipment, the company says it operates across 170 countries and regions while pursuing broader digital infrastructure expansion.

Analysts say China’s current manufacturing capacity for advanced chips remains insufficient to fully satisfy domestic demand, limiting exports in the near term.

However, as manufacturing capacity improves and pricing becomes increasingly competitive, Chinese AI chips could become more attractive in overseas markets, particularly across Southeast Asia.

Counterpoint Research analyst Brady Wang said China’s broader strategy is unlikely to change regardless of whether Nvidia eventually regains broader access to the Chinese market.

“China’s strategy of pursuing technological self-sufficiency — and eventually exporting its technologies — is unlikely to change regardless of whether Nvidia can sell its chips in China,” Wang said.

Tags: Nvidia, Huawei, Artificial Intelligence, China, Semiconductors, AI Chips, Export Controls, DeepSeek, Technology, Corporate Strategy

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Taipei, Taiwan — Nvidia CEO Jensen Huang has sharply criticized U.S. restrictions on exporting advanced computing chips to China, calling the policy a “failure” that is hurting American businesses more than its intended targets.

Speaking at the annual Computex tech conference in Taipei, Huang didn’t mince words when describing the fallout from U.S. export controls. “The fundamental assumptions that led to the AI diffusion rule in the beginning have been proven to be fundamentally flawed,” Huang told reporters.

Nvidia’s Market Share in China Cut in Half

Huang pointed to the dramatic drop in Nvidia’s market share in China as a direct consequence of these restrictions. Once commanding 95% of the Chinese market for advanced chips, Nvidia now holds just 50%. The CEO said that rather than slowing China’s tech ambitions, U.S. policies have fueled a push for self-reliance, with Chinese companies ramping up investment in domestic semiconductor development.

U.S. Policy Shift Sparks Debate

The criticism comes just days after the Trump administration announced it would scrap some of the Biden-era rules that had imposed blanket bans on exports to China. While that move is expected to open the door for new AI deals with countries like Saudi Arabia—deals Nvidia is already eyeing—it also comes with tighter restrictions elsewhere.

Under the revised strategy, the U.S. will still restrict advanced technology exports to dozens of entities, most of them based in China. This includes new licensing rules for specific Nvidia chips and warnings to U.S. firms about using Chinese-made components from companies like Huawei.

Tech vs. National Security: A Growing Divide

Huang’s remarks tap into a broader debate in Washington: how to balance national security with economic competitiveness. Both the Trump and Biden administrations have cited military risks in justifying tech export controls. However, many in the business community argue these rules often end up isolating U.S. companies and encouraging global buyers to look elsewhere.

While former President Trump has championed aggressive trade measures, including sweeping tariffs, he has shown more flexibility with the tech industry—particularly as it played a key role in backing his reelection campaign.

During a recent Middle East tour, Trump touted the loosening of chip export restrictions as a win for American innovation, positioning companies like Nvidia to strike lucrative international deals.

Beijing Fires Back

The U.S. moves have not gone unnoticed in China. In response to the latest crackdown, Beijing accused Washington of violating the trade framework both sides had tentatively agreed upon during negotiations in Switzerland earlier this month.

Chinese officials argue that America’s shifting rules are undermining trust and further destabilizing global tech supply chains.

Bottom Line:

Jensen Huang’s blunt assessment underscores growing frustration in Silicon Valley over how U.S. chip policies are being handled. As global tech competition heats up, the battle over who controls the future of semiconductors is far from over—and American firms may be caught in the middle.

Source: BBC – Nvidia boss calls US chip policy a ‘failure’

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