Large Model Computing Competition Intensifies: New Developments in the Technology Giants' Computing Power Arms Race and Investment Opportunities in 2026

In 2026, the race for large model computing power in the Artificial Intelligence (AI) field is escalating at an unprecedented speed, becoming the core engine driving global technological industrial transformation. As tech giants like OpenAI, Google, and Meta continuously launch more powerful language models and generative AI applications, the demand for computing power is experiencing explosive growth, forcing companies to significantly increase capital expenditure to build stronger infrastructure. This trend is not only reshaping the AI industry chain structure but also bringing new opportunities and challenges for investors. This article will deeply analyze the latest dynamics of the large model computing power race, analyze the strategic layouts of tech giants, and explore their potential impact on the US stock market. ## Surge in Large Model Computing Power Demand: Dual Drivers of Training and Inference The development of large models cannot be separated from the support of massive computing power. Models like OpenAI's GPT-4 and Google's Gemini require thousands of high-performance GPUs to work together during their training process, consuming astronomical amounts of electricity and computational resources. Industry analysis suggests that training a trillion-parameter model might require tens of thousands of GPUs, with costs reaching billions of dollars. This high threshold makes computing power one of the core competencies of tech giants. However, the demand for computing power is not limited to the training phase. With the proliferation of AI applications, the demand for inference is rapidly rising. Inference refers to the process of models handling user requests in actual applications, such as chatbots and image generation. Unlike training, inference requires continuous low latency and high throughput, which places higher demands on computing infrastructure. IDC predicts that the global AI inference server market will grow by 45% in 2026, becoming a new growth point in the computing power market. This trend means that in addition to training computing power, inference computing power is also becoming the focal point for tech giants to compete for. ### Training Computing Power: The Focus of the Arms Race Training large models requires extremely high computing power density and stability. NVIDIA, as the leader in computing chips, sees its H100 and the latest Blackwell series chips as market favorites. However, production limitations lead to shortages of H100s, causing prices to surge. Market news indicates that the rental price for H100 increased by 25% in the second quarter of 2026, highlighting the scarcity of computing resources. To alleviate this pressure, tech giants are building their own data centers, such as Microsoft, Amazon, and Google, planning to invest hundreds of billions of dollars in building hyperscale AI data centers in the coming years. Furthermore, the rise of open-source large models has intensified the computing power competition. After Meta open-sourced its Llama series models, attracting numerous enterprises and research institutions has further increased the demand for computing power. Although these models do not have the parameter scale of commercial giants, they still require a large amount of computing power support through distributed training and optimization. Therefore, whether it is commercial giants or emerging players, are actively laying out computing infrastructure to maintain competitiveness. ### Inference Computing Power: An Emerging Growth Point The demand for inference computing power is mainly driven by the proliferation of AI applications. As generative AI enters enterprise applications, such as customer service, content creation, and autonomous driving, inference computing power becomes a critical bottleneck. Unlike training computing power, inference computing power focuses more on real-time responsiveness and cost-effectiveness. Therefore, tech giants are developing dedicated chips and optimized algorithms to reduce inference costs. For example, Google's seventh-generation TPU "Ironwood" is reported to reduce inference costs by 90%, which provides a possibility for large-scale deployment of inference services. Another trend in computing power is the application of edge computing. To reduce latency and enhance privacy protection, some inference tasks are shifted to edge devices, such as smartphones and IoT devices. This requires chip manufacturers to provide low-power, high-performance solutions, such as NVIDIA's Jetson series and Qualcomm's AI chips. The rise of edge inference brings new growth opportunities to the computing power market, but also imposes higher demands on the supply chain. ## Tech Giants Increase AI Capital Expenditure: Strategic Layout and Supply Chain Restructuring Facing the surge in computing power demand, tech giants are increasing their AI capital expenditure, ushering in a new round of arms race in the computing power market. According to the latest financial reports, Microsoft, Google, and Amazon have all announced significant increases in AI-related investments in the second quarter of 2026, totaling over $50 billion. This funding is mainly used for procuring computing chips, building data centers, and developing AI software. ### Microsoft: Embracing AI Fully, Building Cloud-Edge Collaborative Computing Power Networks As the leader in Azure cloud services, Microsoft is actively positioning itself in AI computing power. In addition to procuring NVIDIA chips, Microsoft is deeply cooperating with OpenAI to provide computing power support. Furthermore, Microsoft announced a 20-year power purchase agreement with Nuclear Energy Company to address data center power shortages. This move not only reduces operating costs but also demonstrates its commitment to long-term computing power supply. Microsoft's AI capital expenditure is mainly used to expand data center scale, enhance computing power density, and develop the Azure AI platform to attract more enterprise customers. ### Google: Parallel Strategy of Chip Self-Development and Open Source Google adopts a dual-track strategy in the field of AI computing power: on one hand, self-developing TPU chips to reduce dependence on NVIDIA; on the other hand, attracting developers through open-source models like Llama. Google's seventh-generation TPU, "Ironwood," shows superior performance in inference, and is expected to secure a place in the inference market. Furthermore, Google's AI capital expenditure focuses on optimizing the TensorFlow and JAX frameworks to improve model training efficiency. Through self-developed chips and an open-source strategy, Google seeks to maintain a leading position in the computing power competition. ### Amazon: Computing Power Advantage of AWS Cloud Services Amazon's AWS cloud services hold a significant position in the computing power market. Its EC2 instances offer various AI acceleration options, including NVIDIA GPUs and self-developed chips. Amazon's AI capital expenditure is mainly used to expand AWS regions and enhance global computing power coverage. Additionally, Amazon invests in AI startups, such as Anthropic, to enhance its influence in the generative AI field. Through cloud services, Amazon not only meets its own needs but also provides computing power solutions to other enterprises, further solidifying its market position. ## Computing Power Supply Chain Challenges: Chip and Power Bottlenecks Despite the increased investment by tech giants, the computing power supply chain still faces numerous challenges. Firstly, there is chip supply tightness. The production delay of NVIDIA's Blackwell Ultra chips has led to insufficient market supply. Although AMD's MI400X chips attempt to challenge NVIDIA, their production ramp-up is slow, making it difficult to meet demand. Moreover, advancements in chip manufacturing processes face physical limits; the production difficulty of 3nm and below processes exacerbates chip shortages. Secondly, there is the issue of power supply. Hyperscale data centers consume vast amounts of electricity, leading to rising electricity costs and carbon emission pressures. Microsoft's nuclear power purchase agreement is one measure to address this challenge, but nuclear construction takes a long time, making it difficult to alleviate power shortages in the short term. Other solutions include renewable energy and liquid cooling technology, but these require time and capital investment. The power bottleneck has become a key constraint on computing power development. ## Investment Opportunities: A Panoramic Analysis of the US Stock AI Computing Chain The large model computing competition brings rich investment opportunities to investors. From chip manufacturers to data center operators and AI software companies, the entire industry chain benefits from the growth in computing power demand. Here are some key investment recommendations:

• Leading Computing Chips: NVIDIA (NVDA) and AMD (AMD) are core beneficiaries. NVIDIA holds a dominant market position with its H100 and Blackwell series chips, while AMD's MI400X chip is expected to capture a share in the inference market. Furthermore, Broadcom's switch chips (such as 51.2T chips) are important investment targets as they address the computing network bottleneck.
• AI Server Chain: Server manufacturers such as Super Micro Computer (SMCI) and Dell Technologies (DELL) benefit from the growth in computing power demand. These companies provide high-performance AI servers to meet training and inference needs. As the inference market expands, edge servers are becoming a new growth point.
• Data Center Operators: Such as Digital Realty (DLR) and Equinix (EQIX), they provide data center infrastructure supporting the computing power needs of tech giants. With the expansion of hyperscale data centers, the rental income of these companies is expected to grow.
• AI Software and Platforms: Such as Databricks, whose AI data platform is set to file an IPO in the US in 2026, aiming for a trillion-dollar valuation. Databricks provides data lake warehousing and AI model training services, benefiting from the growth in computing power demand. Additionally, AI security companies like Palo Alto Networks (PANW) are worth watching, as the demand for network security increases with the proliferation of AI applications.

## Risks and Challenges: Market Volatility and Intensified Competition Despite numerous investment opportunities, the computing power market also has risks. Firstly, there is the risk of technological iteration. Rapid progress in AI chips and algorithms may lead to the devaluation of existing investments. For example, a potential delay in NVIDIA's Blackwell Ultra could cause short-term supply issues, affecting stock prices. Secondly, is intensified competition; new entrants like AMD and Google may capture market share, leading to price wars. Furthermore, macroeconomic factors such as changes in interest rates and trade policies may affect computing power investments. An interest rate hike cycle could increase corporate financing costs and suppress capital expenditure. Trade restrictions could affect the chip supply chain, leading to cost increases. Investors need to closely monitor these risks and adopt a cautious approach. ## Conclusion: Long-Term Impact of the Computing Power Competition and Future Outlook Large model computing power competition is a significant feature of the AI era; it not only drives technological progress but also reshapes the industrial landscape. Increased capital expenditure by tech giants, supply chain restructuring, and investment opportunities bring new growth momentum to the US stock market. However, power bottlenecks and chip supply challenges still need to be resolved. In the future, with the popularization of AI applications and technological maturity, the computing power market is expected to continue to expand, providing long-term value for investors. For investors, focusing on core companies in the computing power chain, such as NVIDIA, AMD, and data center operators, will be key to capturing this trend. Simultaneously, vigilance against short-term fluctuations is necessary, maintaining a long-term perspective. The large model computing power competition is not just a technological contest but also a contest of capital and strategy; only those companies that can continuously innovate and optimize costs can win in the competition.