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Global enterprise AI expenditure is expected to reach about $940 billion in 2026 and climb to roughly $2.1 trillion by 2029, reflecting a shift from experimental deployments to large-scale use across manufacturing, finance, logistics, health care, energy and public services. China is emerging as one of the fastest-expanding markets within that cycle, helped by heavy infrastructure spending, state-backed industrial policy and a corporate race to build domestic alternatives to foreign chips, models and cloud platforms.

The acceleration is no longer confined to model development. China’s AI buildout is moving across the full technology stack, from data centres and accelerators to enterprise software, robotics and agentic systems capable of handling complex workflow tasks. The first phase of the investment wave was dominated by computing power, foundational models and large infrastructure projects. The next phase is being shaped by applied AI, intelligent services and automation across production lines, offices and consumer platforms.

That transition is putting pressure on global rivals. US technology groups still lead in advanced chips, frontier models and hyperscale cloud capacity, but China’s market has advantages in deployment speed, manufacturing depth and policy coordination. Companies including Alibaba, Tencent, Baidu, Huawei and ByteDance are expanding AI services while also trying to reduce dependence on restricted US semiconductor supplies. Alibaba has unveiled a new AI chip through its T-Head unit and is directing tens of billions of dollars into cloud and AI infrastructure over several years. Huawei’s Ascend processors have become central to China’s domestic accelerator strategy as export controls limit access to top-tier Nvidia products.

Infrastructure remains the foundation of the race. Worldwide AI infrastructure spending is on course to exceed $1 trillion by 2029, with accelerated servers accounting for the overwhelming share. China remains the second-largest AI infrastructure market after the US, even though export restrictions have created supply constraints for high-end accelerators. Those restrictions have encouraged domestic chip design, local server ecosystems and more efficient deployment models for inference, where many commercial AI applications run after models have been trained.

China’s data centre expansion is also moving into more specialised formats. AI workloads demand dense computing power, stable electricity supply and advanced cooling, all of which are raising costs and straining grids. Projects near Shanghai and other technology hubs are testing alternative energy and cooling systems, including offshore and renewable-powered facilities. These efforts reflect a broader attempt to manage the energy burden of AI while supporting national ambitions in cloud services, industrial software and smart manufacturing.

Robotics is becoming a central pillar of the spending wave. China already has the world’s largest industrial robot base and is pushing into embodied intelligence, where AI systems are integrated into machines that operate in factories, warehouses, homes and public spaces. Forecasts for China’s robotics market point to rapid expansion over the next five years, with demand coming from electric vehicles, electronics, logistics and elder care. The combination of AI models, sensors, batteries and manufacturing scale gives domestic firms a route to compete internationally beyond software alone.

Policy support is reinforcing corporate investment. Beijing’s “AI Plus” strategy, issued in 2025, aims to embed artificial intelligence across the economy by expanding access to data, compute and talent while promoting open-source ecosystems and industrial adoption. Local governments have added funding programmes, innovation parks and procurement support, creating competition among cities such as Beijing, Shanghai, Shenzhen and Hangzhou. That model can accelerate deployment, though it also raises the risk of duplicated capacity, uneven standards and projects driven more by subsidy incentives than commercial demand.

The competitive picture remains mixed. China leads in AI publication volume, citations, patent output and industrial robot installations, while the US continues to produce more frontier models and higher-impact patents. The performance gap between leading US and Chinese models has narrowed, aided by open-source releases and engineering efficiency from Chinese labs. DeepSeek’s breakthrough earlier in the cycle demonstrated that capable systems could be produced with fewer resources than previously assumed, intensifying debate over whether raw spending alone will determine leadership.

Huang is expected to meet Taiwan Semiconductor Manufacturing Company chairman and chief executive C. C. Wei as Nvidia prepares a second-half production ramp for Vera Rubin, the next-generation platform that combines new Vera CPUs, Rubin GPUs, advanced memory and high-speed networking for rack-scale AI systems. The visit has drawn close attention because Nvidia’s product cycle is now tied more tightly than ever to Taiwan’s foundries, packaging houses, server assemblers and component suppliers.

Nvidia reported revenue of $81.6bn for the quarter ended April 26, up 85 per cent from a year earlier, with data-centre revenue reaching $75.2bn, up 92 per cent. The company also authorised an additional $80bn in share repurchases and raised its quarterly dividend to 25 cents a share from 1 cent, underscoring the scale of cash generation from the AI infrastructure boom.

Demand is being driven by cloud providers, AI model developers, enterprise customers and sovereign AI projects, all seeking clusters capable of training and running larger generative and agentic AI systems. Nvidia has described the buildout of “AI factories” as one of the largest infrastructure expansions in technology history, with its platform spanning chips, networking, systems software and reference designs for data centres.

Vera Rubin is expected to follow the Blackwell and Grace Blackwell generation, which is still being shipped in high volumes. That overlap is creating pressure across Taiwan’s supply chain because new capacity must support existing commitments while preparing for a platform with different requirements in advanced packaging, high-bandwidth memory integration, board assembly, cooling and system validation.

Huang has described Vera Rubin as potentially the biggest product ramp in Nvidia’s history and one of the largest technology manufacturing efforts Taiwan has handled. Industry estimates around the platform point to millions of parts across each system and the involvement of well over 100 Taiwan-based ecosystem partners, from chip fabrication and advanced packaging to printed circuit boards, power systems, connectors, thermal modules and final server integration.

TSMC remains central to the effort because Nvidia depends on its leading-edge process technology and advanced packaging capacity. The challenge is not limited to wafers. CoWoS and other packaging technologies have become strategic bottlenecks as AI accelerators require multiple logic dies, high-bandwidth memory stacks and dense interconnects. Any delay in packaging expansion can ripple through server production schedules, cloud deployment plans and Nvidia’s revenue timing.

Taiwan’s broader technology sector has become a critical artery for the global AI trade. TSMC, Foxconn, Quanta, Wistron, Wiwynn, Inventec and a dense network of specialist suppliers are competing to add capacity while dealing with constraints in land, power, water, skilled labour and equipment availability. The concentration gives Nvidia speed and engineering depth, but also exposes the company to geopolitical and operational risks.

China remains the clearest commercial setback. Huang has said US export controls have pushed Nvidia’s share of China’s advanced AI chip market to zero, a striking reversal for a company that once held a dominant position there. The restrictions have strengthened the opening for Huawei and other domestic suppliers, while leaving Nvidia dependent on whether Washington and Beijing allow compliant chips to be sold without triggering national-security objections.

That loss has not slowed Nvidia’s global momentum, but it has changed the composition of growth. Revenue is now increasingly concentrated in markets where hyperscale cloud companies, enterprise AI developers and governments are building large AI data centres outside China. For investors, the question is whether demand can keep absorbing each successive architecture at the scale implied by Nvidia’s guidance.

Nvidia forecast revenue of about $91bn for the current quarter, above market expectations, signalling that orders for AI infrastructure remain strong despite concerns about overcapacity, power shortages and the pace at which customers can monetise AI services. Operating expenses rose 49 per cent to $7.75bn, reflecting the growing cost of research, supply commitments and ecosystem investments needed to sustain the company’s lead.

The Tokyo-listed investment group has become one of the most closely watched proxies for AI demand after committing tens of billions of dollars to OpenAI and building exposure across chips, power infrastructure, robotics and data-centre supply chains. The latest rally extended a sharp two-session advance that added tens of billions of dollars to SoftBank’s market value, marking one of its strongest runs since the technology boom that first made the group a symbol of risk-taking capital.

Investor enthusiasm has been driven by two linked expectations. OpenAI is preparing for a confidential US initial public offering filing that could set up a listing as early as September, while SB Energy, a power and data-centre infrastructure company backed by SoftBank and OpenAI, has said it intends to confidentially file for a US IPO. Together, the developments have strengthened the market view that SoftBank’s private holdings may be moving closer to public-market price discovery.

SoftBank’s OpenAI position is the main driver of that reassessment. The group has invested $34.6bn in the ChatGPT maker since September 2024 and has agreed to make a further $30bn investment through SoftBank Vision Fund 2. Completion of those tranches would lift its cumulative investment to $64.6bn for an ownership interest of about 13 per cent. The preferred shares are designed to convert automatically into common shares if OpenAI completes an IPO or related listing transaction.

OpenAI’s valuation has become central to SoftBank’s investment case. The company was valued at $852bn in private markets and preliminary discussions around a listing have included the possibility of a valuation as high as $1tn. The company is also seeking to raise at least $60bn at the lower end of earlier discussions, reflecting the huge capital needs of frontier AI model development, data centres, chips and power supply.

The OpenAI bet has already reshaped SoftBank’s earnings profile. The group reported a sharp profit rebound, helped by valuation gains tied to OpenAI, and said cumulative gains on the investment had reached $45bn. Its annual profit of about ¥5tn was described by the company as the highest recorded by a Japanese company, underscoring how quickly Son’s AI strategy has altered investor perceptions after years of scrutiny over the Vision Fund’s uneven performance.

SB Energy has added another layer to the rally because its business sits at the intersection of AI and power infrastructure. Founded in 2019, the company develops large-scale energy and data-centre projects and has positioned itself to benefit from surging electricity demand linked to AI workloads. It has raised more than $18bn in project capital and has a portfolio of about 5 gigawatts of operating and under-construction energy assets.

The company’s January partnership with OpenAI under the Stargate initiative placed it inside a broader push to build AI and energy infrastructure in the United States. That has made SB Energy more than a conventional renewable power developer in the eyes of investors. Its appeal rests on the argument that AI expansion will require dedicated power generation, grid connections and purpose-built campuses capable of supporting high-density computing.

SoftBank’s rally has also been supported by strength in Arm Holdings, the UK chip designer in which the group holds a large stake. Arm remains a key public asset for SoftBank and is viewed as a beneficiary of AI-related semiconductor demand. Gains in Arm shares have helped reinforce confidence that SoftBank owns multiple assets tied to the same structural theme, rather than a single concentrated wager on OpenAI.

The optimism carries risks. OpenAI faces intensifying competition from Alphabet’s Gemini, Anthropic’s Claude and other model developers, while the cost of training and operating advanced AI systems continues to rise. Credit analysts have raised concerns that the size of SoftBank’s OpenAI commitment may affect liquidity and asset quality, particularly as the group uses loans, asset sales and financing backed by holdings such as Arm and SoftBank Corp. to fund its strategy.

SoftBank arranged a $40bn bridge loan in March and drew $20bn in April, largely for the OpenAI investment, while also repaying part of that borrowing. The group has sold stakes in holdings including Nvidia and T-Mobile, issued bonds and considered other financing options as it reallocates capital towards AI. Chief Financial Officer Yoshimitsu Goto has said the group may also explore ways to use its OpenAI assets for financing if market conditions allow.

The investment is designed to strengthen the chipmaker’s partnerships with foundries, packaging houses, substrate suppliers and server manufacturers that are central to its next generation of AI systems. The programme places Taiwan at the centre of AMD’s effort to scale production of high-performance processors, accelerators and rack-level systems built for increasingly complex AI workloads.

A major focus will be Elevated Fanout Bridge, or EFB, a 2.5D packaging technology intended to improve the speed and efficiency of connections between chip components. AMD plans to use the technology in its sixth-generation EPYC central processing units, codenamed “Venice”, which are being positioned for AI, cloud and high-performance computing deployments.

The move comes as AI infrastructure spending shifts from individual chips towards complete systems that combine CPUs, graphics processors, high-bandwidth memory, networking and software. Advanced packaging has become a critical bottleneck because AI workloads require large volumes of data to move quickly between processors while keeping power consumption and heat within manageable limits.

AMD Chair and Chief Executive Lisa Su said customers were rapidly expanding AI infrastructure to meet rising compute demand. The company is seeking to combine its high-performance computing portfolio with Taiwan’s manufacturing and packaging capabilities to support integrated, rack-scale AI systems.

AMD is working with partners including ASE, SPIL, Powertech Technology, Wiwynn, Wistron, Inventec, Unimicron, AIC, Nan Ya PCB and Kinsus. These companies cover key parts of the supply chain, from advanced packaging and substrates to server assembly and mechanical architecture. Sanmina is also involved in manufacturing support for the company’s Helios platform.

The investment will support both wafer-based and panel-based EFB interconnect development. AMD and Powertech Technology have qualified what they describe as the industry’s first 2.5D panel-based EFB interconnect, a step aimed at improving scalability and production economics for high-volume AI processors.

The technology is expected to help the “Venice” EPYC processors deliver higher interconnect bandwidth and better power efficiency. That matters because data centre operators are increasingly constrained by electricity availability, cooling capacity and rack density, not only by chip performance.

AMD has also begun ramping production of “Venice” CPUs using Taiwan Semiconductor Manufacturing Company’s 2-nanometre process technology in Taiwan. Future production is planned at TSMC’s Arizona facility, extending the supply chain beyond Taiwan while preserving the company’s close reliance on TSMC’s leading-edge manufacturing.

The “Venice” chips will be a central element of AMD’s Helios rack-scale platform, which also includes AMD Instinct MI450X graphics processors, advanced networking and the ROCm open software stack. The platform is scheduled for deployments beginning in the second half of 2026, with AMD describing the opportunity in terms of multi-gigawatt AI infrastructure rollouts.

The strategy puts AMD in a more direct contest with Nvidia, which dominates AI accelerator sales and has built a strong lead through its GPUs, networking, software ecosystem and full-rack systems. AMD’s challenge is not only to deliver competitive silicon but also to prove that it can scale complete AI infrastructure with dependable supply, efficient packaging and broad software support.

AMD’s data centre business has become the company’s main growth engine. First-quarter 2026 data centre revenue reached $5.8 billion, up 57 per cent from a year earlier, driven by demand for EPYC processors and the ramp-up of Instinct GPU shipments. That performance has strengthened investor expectations that AMD can capture a larger share of AI and cloud infrastructure spending.

Taiwan remains critical to that effort because of its concentration of semiconductor expertise. TSMC anchors the foundry side, while packaging, substrate and system-assembly partners provide the manufacturing depth needed to turn chip designs into deployable infrastructure. The concentration also creates geopolitical and supply-chain risks, particularly as governments and companies seek greater resilience across strategic technologies.