Tencent has reportedly signed its largest overseas cloud leasing agreement with Oracle, securing access to about 100,000 advanced artificial intelligence chips through data centers in Southeast Asia as Chinese technology companies seek additional computing capacity outside the country.

The five-year agreement, reported by the Financial Times on Wednesday, citing people familiar with the matter, is estimated to be worth about $7 billion and would involve multiple Oracle data centers across Southeast Asia. Tencent is also expected to make an upfront payment of about 30%, according to the report.

If confirmed, the scale of the arrangement would highlight the extraordinary computing requirements emerging from China’s AI race, as Tencent and other major technology companies invest heavily in training and deploying more capable models.

More importantly, the reported structure illustrates how Chinese technology companies are seeking access to advanced AI computing capacity through infrastructure outside mainland China as U.S. restrictions limit the availability of leading AI processors inside China.

But Tencent’s reported decision to lease computing capacity in Southeast Asia rather than simply expand its domestic infrastructure points to a broader challenge facing China’s AI industry.

The United States has imposed export controls restricting China’s access to some of the most advanced AI chips, particularly processors from Nvidia and other U.S. suppliers. Beijing has responded by accelerating development of domestic alternatives, while Chinese companies have simultaneously sought other ways to obtain computing capacity.

A five-year Oracle agreement worth roughly $7 billion would mark a substantial commitment to overseas AI infrastructure. The reported 100,000 chips would give Tencent access to a large pool of advanced computing resources that are unavailable to it domestically.

The arrangement would also shift part of Tencent’s AI infrastructure footprint outside China.

This is considered a huge shift because, among other things, training large language models requires enormous amounts of computing power, while running AI products at scale also requires sustained access to inference capacity. As models become more capable and AI applications gain users, the computational burden increasingly extends beyond the initial training phase.

Tencent has been expanding its AI ambitions across consumer and enterprise products, increasing the need for both model-training infrastructure and computing capacity for commercial deployment.

The company recently released a preview version of a new AI image-generation model aimed at professional creators. The model supports text-to-image and image-to-image generation, adding to Tencent’s growing portfolio of AI applications.

The Economics of The Chip Squeeze

The reported deal also illustrates how export restrictions can change the economics of AI development.

For Chinese companies, access to cutting-edge processors is not simply a question of purchasing chips. Computing capacity can be obtained through cloud providers that operate data centers in jurisdictions where certain processors can legally be deployed.

Tencent would reportedly be leasing computing capacity from Oracle rather than importing the processors directly into China. The structure could allow the company to use advanced chips without those processors being physically deployed inside mainland China.

The reported arrangement thus underpins the growing importance of cloud infrastructure as an intermediary between semiconductor restrictions and AI development. It also reveals why Washington has increasingly focused not only on direct chip exports but on the possibility that restricted Chinese companies could obtain access to advanced computing remotely through overseas data centers.

For cloud providers, meanwhile, the surge in AI demand creates an enormous infrastructure opportunity. Oracle has been expanding its cloud capacity to serve AI developers and has signed large computing agreements with technology companies seeking access to scarce advanced processors.

Tencent’s reported commitment is expected to add another major customer to that trend.

China’s AI Race is Becoming An Infrastructure Race

The deal comes as Chinese technology companies compete to develop more capable AI models while Beijing encourages greater reliance on domestic technology.

China’s strategy is producing two parallel efforts.

One is the development of domestic processors and software ecosystems that can reduce reliance on Nvidia and other foreign suppliers. The other is securing access to advanced computing resources wherever they remain available.

Tencent is one of China’s largest technology companies and has substantial financial and engineering resources to devote to AI. Its willingness to reportedly commit billions of dollars to overseas cloud capacity underscores how valuable advanced computing has become.

The scale of the reported transaction also puts the economics of the AI boom into perspective. A $7 billion commitment over five years would amount to roughly $1.4 billion a year, before accounting for other infrastructure, energy, networking, personnel and model-development expenses.

That spending underlines why AI is becoming an increasingly capital-intensive business even for companies that already possess extensive cloud and data-center infrastructure.

Nevertheless, the investment is expected to provide Tencent with the computing capacity needed to accelerate model development and support large-scale deployment. For Oracle, a deal of this size would reinforce the importance of cloud infrastructure providers in the global AI supply chain.

But the reported agreement also underscores the limits of China’s current domestic chip ecosystem. If Tencent needs to secure tens of thousands of advanced processors through overseas infrastructure, it suggests that domestic alternatives have not yet fully eliminated the computing gap created by U.S. restrictions.

China’s technology industry is therefore pursuing two tracks simultaneously: developing its own AI hardware and software while finding ways to access global computing capacity. The Tencent-Oracle agreement, if confirmed, would be one of the clearest examples yet of how those two pressures are reshaping the geography of AI infrastructure.

The United States may have an advantage in developing the world’s most advanced artificial intelligence models, but constraints on electricity and data-center infrastructure could limit how quickly those models are deployed, creating an opening for China to narrow the gap, according to Citadel Securities.

Nohshad Shah, head of EMEA fixed-income sales at Citadel Securities, said in a September 26 blog post that the AI race will ultimately depend on more than the quality of the underlying models. The ability to deploy AI across factories, vehicles, robots, drones and industrial systems could prove equally important, particularly if China can pair cheaper models with a much larger physical infrastructure base.

“China does not need the world’s best model in every domain if it can combine a slightly less capable (and much cheaper) one with more factories, robots, vehicles, drones, and industrial equipment,” Shah wrote.

The argument underpins a distinction between AI development and AI deployment. The United States has built a strong position in frontier-model development through companies such as OpenAI, Anthropic, and other leading AI laboratories. But deploying those models at scale requires enormous amounts of electricity, data-center capacity, semiconductor infrastructure, and physical facilities.

The infrastructure gap has resulted in a potential bottleneck for the US at a time when demand for computing power is accelerating.

Shah identified electricity as the most significant constraint. Data centers supporting powerful AI systems require large and reliable supplies of power, while projects in the US are encountering delays involving grid connections, permitting, and opposition from local communities.

China, meanwhile, is expanding its electricity-generating capacity at a substantially faster pace.

“China will add almost six times as much power-generation capacity as the US over the next five years, whilst America remains constrained by grid connections, permitting, and local opposition to data centers,” Shah wrote.

“That creates an uncomfortable possibility: America develops the better models but China has more places to run it.”

The comparison has become necessary because the economic value of AI will increasingly depend on how widely the technology can be integrated into the real economy. A more capable model does not necessarily translate into greater economic impact if there is insufficient computing and electricity capacity to run it at scale.

China’s manufacturing base could further amplify that advantage. AI systems can be incorporated into industrial robots, autonomous vehicles, drones, and production equipment, creating demand for inference capacity outside traditional cloud-computing environments.

That could allow a country with slightly less advanced models to generate substantial economic value through much broader deployment.

The infrastructure issue is becoming more significant as the AI industry pushes toward larger models and increasingly compute-intensive applications. Data centers are already placing new demands on electricity grids, while technology companies and infrastructure developers compete for power, land and transmission capacity.

In the US, opposition to new data centers has also emerged as a political and community issue. Shah said local resistance, together with permitting constraints and grid limitations, could become a greater threat to the country’s AI ambitions.

He argued that the next major constraint on AI development may not be chips or computing hardware but permission to build the infrastructure required to operate them.

Shah made a similar argument in August, saying that regulation and permitting could become the next major AI bottleneck.

His latest assessment also challenges the assumption that the country with the most sophisticated frontier models will automatically dominate the AI economy. Development and deployment are separate stages of the technology’s expansion, and the second requires physical infrastructure that cannot be produced simply by improving software.

Shah also connected the infrastructure challenge to the increasingly prominent debate over AI safety and potential social disruption. He argued that warnings about AI eliminating jobs or creating extreme risks could make it harder for the industry to secure the public support needed for new data centers and power infrastructure.

“The mistake is to tell the public that AI may eliminate their jobs (or indeed humanity itself!) and then ask the same public to provide the land, electricity and permits required to build it,” Shah wrote.

“The industry has spent several years making the strongest possible case for why AI is powerful and the weakest possible case for why ordinary people should want it.”

Shah does not argue that China is certain to overtake the US. Rather, he said the US can maintain its lead, but doing so would require greater political support for infrastructure development and fewer regulatory obstacles.

The emerging competition now extends beyond the race to build more capable AI models. It is also becoming a contest over electricity generation, transmission networks, data centers, industrial capacity, and the ability to deploy AI across the wider economy.

The challenge for the US is that technological leadership can be undermined if physical infrastructure cannot keep pace with the rapid growth in computing demand. For China, a less advanced model ecosystem could become less of a disadvantage if its expanding power and industrial base allow it to deploy AI more extensively.

The divergence has made the power grid an important part of the global AI race, alongside chips, models and capital.