1. Core of Cooperation: Four-Year Binding Agreement on Equity and Procurement
On October 6, 2025, OpenAI and AMD officially disclosed their strategic cooperation, which includes two main dimensions. In terms of computing power deployment, OpenAI will implement a total of 6 gigawatts of AMD Instinct GPU computing power over the next four years, with the first batch of 1 gigawatt based on the MI450 series chips scheduled to start deployment in the second half of 2026, corresponding to a procurement scale of hundreds of thousands of chips. In terms of interest binding, OpenAI will receive up to 160 million AMD stock warrants, which, when exercised at $0.01 per share, will give it approximately 10% of AMD’s total equity, making it a significant strategic shareholder.
AMD’s CEO, Lisa Su, clearly stated that this agreement will bring the company hundreds of billions of dollars in new revenue each year over the next four years, and with the associated effects, the total new revenue is expected to exceed $100 billion. OpenAI’s President, Greg Brockman, pointed out that the cooperation aims to alleviate the bottleneck of computing power that limits the functionality of products like ChatGPT and to build a diversified computing power foundation. Following the announcement, AMD’s stock price surged 23.71% in a single day, with its market value increasing by $63.4 billion, marking the largest increase in nearly a decade.
2. Analysis of Motivations: Dual Drivers of Computing Power Demand and Supply Chain Security
On the demand side, OpenAI’s consumption of computing power is growing exponentially. The training of GPT-4 used about 25,000 NVIDIA A100 GPUs, while the rumored computing power required for GPT-5 is more than ten times that of its predecessor. However, the delivery cycle for NVIDIA’s advanced GPUs is as long as 8-12 months, and production capacity must compete with companies like Google and Meta, directly restricting the pace of product development. Currently, OpenAI’s valuation has reached $500 billion, with over 700 million weekly active users of ChatGPT globally, and more than 92% of the Fortune 500 companies developing based on its products, making computing power support a core competitive advantage.
On the supply chain side, NVIDIA holds over 86% of the AI accelerator market, creating a seller’s market. Some cloud service providers must accept “bundled sales” terms to gain priority supply, pairing high-end GPUs with network equipment and software licenses, which raises procurement costs. OpenAI’s choice to deeply cooperate with AMD is not to replace NVIDIA—just two weeks ago, it signed a $100 billion equity and supply agreement with NVIDIA—but rather to establish a second supply chain through a dual-supplier strategy to reduce the risk of single dependency.
3. Impact on the Landscape: Structural Shift from “One Dominant Player” to “Multiple Strong Players”
Previously, the global AI chip market exhibited a significant “one dominant player and many strong players” pattern, with NVIDIA occupying over 90% of the AI training market share due to its CUDA ecosystem and hardware advantages, expecting to achieve $206.26 billion in revenue in the 2025 fiscal year, while AMD’s expected revenue during the same period is only $32.78 billion, showing a stark contrast. As the highest-valued AI company globally, OpenAI’s recognition of AMD’s technology roadmap sends a clear signal effect, prompting other large AI laboratories and cloud service providers to reassess the technical feasibility of AMD.
Market competition has extended from a single-point computing power comparison to a system-level ecosystem. This cooperation clearly includes suppliers of HBM and UALink technologies such as Samsung and Astera Labs in the beneficiary chain, indicating that AI chip competition has covered the entire chain, including storage and interconnects. At the same time, a trend towards industry diversification has emerged: Meta plans to deploy over 600,000 GPUs, covering both NVIDIA products and its self-developed MTIA chips; Microsoft Azure supports NVIDIA, AMD, and its self-developed Maia chips; Google relies on its TPU series for computing power autonomy, gradually ending the era dominated by a single supplier.
4. Future Variables: Triple Test of Production Capacity, Ecosystem, and Competitive Strategy
For AMD, the core challenge lies in delivering production capacity and technical adaptation. It must deliver the first batch of MI450 chips on schedule in the second half of 2026 and meet OpenAI’s stringent requirements for computing power density and power consumption control. This cooperation also deeply binds its performance to the AI market cycle, facing the risk of market bubbles. Currently, AMD’s AI GPU revenue for 2025 is expected to be $6.55 billion, and this business will achieve a significant leap after the cooperation is implemented.
For NVIDIA, its short-term ecological advantage remains difficult to shake, as its CUDA software ecosystem and rack-level deployment experience constitute a core barrier. However, it cannot be ruled out that it may respond to competition through price reductions or accelerated ecological upgrades. OpenAI must balance the technical synergy of the dual-supplier route to avoid efficiency losses caused by fragmented computing power architecture.
It is worth noting that this cooperation reflects the embryonic form of an AI “closed-loop economy”: OpenAI anchors computing power demand, companies like AMD provide chips, Oracle builds data centers, and capital, equity, and computing power form a circular flow among leading enterprises. This model may define the competitive rules of future AI infrastructure.