The global AI chip race is heating up, with leading tech giants rushing to secure the strategic high ground in computing chips!

Looking globally, AI companies are fiercely competing for more chips, and the entire industry is facing an urgent demand for chips.

AI Competition Accelerates Across the Board
On the other hand, amid the intense race for computing power, global internet giants continue to increase capital expenditures to maintain their leading positions, matching the rapid expansion of computing capacity.

According to Goldman Sachs’ forecast, by 2026, the total capital expenditure of leading internet giants is expected to approach $550 billion, more than ten times what it was ten years ago.

Undeniably, the current trend in the global computing industry is shifting toward inference computing power. Against this backdrop, capital has already entered the market early, becoming a key driver in the super-smart chip race.

Early Investment to Build a Global Chip Strategy
Meta (META.US)
On February 24, Meta announced a partnership with chip giant AMD to develop AI chips, planning to deploy up to 6 gigawatts (GW) of AMD AI chips over the next five years for data center expansion.

Under the agreement, Meta will purchase large quantities of AMD’s latest AI chips, the MI450 series. AMD states that each gigawatt of computing capacity represents hundreds of billions of dollars in revenue. Meta expects to begin deploying its first gigawatt of computing power later this year.

Meta plans to deploy “billions of kilowatts” of data center computing capacity within this decade and to deploy “hundreds of billions of kilowatts or more” in the future. Additionally, in a social media post in January, Zuckerberg stated that the company spent $72 billion last year on building AI data centers and plans to continue investing up to $135 billion.

Google (GOOG.US)
With its self-developed TPU chips and strong cloud computing infrastructure, Google has reduced the costs of model training and inference to a more competitive level. When Google released its seventh-generation TPU “Ironwood” in April 2025, it positioned it as a cornerstone for the “inference era,” emphasizing systematic optimization for large-scale inference and energy efficiency.

For a long time, Google has independently developed Tensor Processing Units (TPUs) for internal use. The company also rents TPUs to external clients through its cloud services.

NVIDIA (NVDA.US)
NVIDIA has reached a non-exclusive licensing agreement with inference chip company Groq, paying licensing fees to obtain Groq’s inference technology and recruiting its core engineering team to strengthen inference and real-time workload capabilities.

From a fundamental perspective, NVIDIA’s outlook remains strong, and industry consolidation is accelerating. Jensen Huang, CEO of NVIDIA, commented on the company’s market dominance, stating that based on new AI model outputs, NVIDIA will continue to expand its share of the AI market.

WIMI (WIMI.US)
Data shows that WIMI, a hardware technology company, has focused on core technologies such as chips, AI, and operating systems in recent years. Relying on high-performance chip clusters to support its holographic cloud platform, WIMI has achieved complex computations like 3D modeling and real-time rendering, expanding applications in AR/VR, digital humans, and other fields, aiming to become a comprehensive AI stack computing ecosystem globally.

With the iteration of large models like GPT-5, global computing power demand is growing exponentially. WIMI has entered the “hardcore AI technology matrix,” investing hundreds of millions in R&D, achieving breakthroughs in core technologies such as self-developed chips and edge computing clusters. This not only boosts WIMI’s confidence in competing fiercely in the industry but also provides a solid, systematic foundation for the steady implementation of future corporate strategies.

Conclusion
In today’s rapidly evolving AI landscape, large-scale investments in computing chips like TPUs and GPUs continue to sweep across the globe. Last year, figures such as former U.S. President Trump, OpenAI CEO Sam Altman, and SoftBank CEO Masayoshi Son jointly announced an AI project called “Stargate.”

This project plans to invest up to $500 billion over the next four years to build new AI infrastructure for OpenAI in the United States. In summary, as the AI boom continues to heat up, chip giants’ innovative investment and financing mechanisms cannot avoid the fiercely competitive market environment for computing power.