On January 14, 2026, the US stock market experienced severe volatility amid a confluence of multiple negative factors, with all three major indices closing lower. The Nasdaq Composite led the decline, falling approximately 1%. The technology sector, a core market driver, bore the brunt of the day's sell-off, with the semiconductor industry particularly hard-hit by a double blow from geopolitical tensions and shifting competitive dynamics. Notably, a landmark $10 billion deal between OpenAI and AI chip startup Cerebras Systems, involving a 750-megawatt power supply for computing, objectively contributed to amplifying the tech stock downturn.
Specifically, OpenAI entered into a multi-year strategic collaboration with Cerebras to secure computing power equivalent to a 750-megawatt power draw through hardware provided by the latter, aiming to accelerate the rapid expansion of its artificial intelligence infrastructure. According to a joint statement released by the two companies on Wednesday, OpenAI has integrated Cerebras into its compute supplier ecosystem, a move intended to achieve shorter response times for AI models during operation. This infrastructure will be built in phases, with full deployment expected by 2028, and will be hosted and operated by Cerebras.
Although specific financial terms were not disclosed, people familiar with the matter revealed that the total scale of the cooperation agreement exceeds $10 billion. Greg Brockman, OpenAI's co-founder and president, stated, "Through this collaboration, ChatGPT will not only become the most capable AI platform but will also achieve comprehensive leadership in speed." He further emphasized that this breakthrough in response speed will effectively unlock "the next generation of AI application scenarios" and accelerate the integration of the "next billion users" into the AI ecosystem.
Packing AI onto a "Wafer Plate": How Cerebras' WSE-3 Delivers 15x Inference Speed for OpenAI It is understood that semiconductor startup Cerebras, a pioneer in unique information processing architecture based on ultra-large chips, is actively promoting the widespread application of its technology to challenge market leader NVIDIA. Unlike the "cluster-based" technical path adopted by NVIDIA, Cerebras has chosen an innovative "Wafer-Scale" direction. Its flagship product, the WSE-3, is a single chip the size of a dinner plate (with an area of 46,255 square millimeters), manufactured directly on an entire 12-inch silicon wafer.
Cerebras' core technical logic is that, rather than having data travel long distances between thousands of individual chips, it is better to integrate over 900,000 AI cores and massive memory units onto the same giant chip, thereby completely eliminating the performance bottlenecks associated with inter-chip communication. In terms of application scenarios, Cerebras' core advantage manifests as extreme inference speed performance. For example, when running ultra-large models like Llama 4, its single-chip architecture can achieve low-latency output of nearly a thousand words per second, a characteristic that makes it inherently suitable for scenarios with stringent real-time interaction requirements, such as agent decision-making and real-time code generation.
More importantly, developers do not need to deal with the complexities of distributed cluster configuration; deployment can be accomplished using a programming model akin to that of a single computer, significantly lowering the barriers to AI model development and operation. Overall, if NVIDIA is likened to "Lego bricks"—where you can build a skyscraper by stacking countless bricks, albeit with some loss at the connections, but with great flexibility—then Cerebras is "a single, massive piece of rock." It is structurally dense and highly efficient, but once formed, it cannot be easily split apart, and its manufacturing is extremely challenging.
This is precisely why OpenAI chose to partner with Cerebras: in 2026, as AI models shift towards "real-time interaction," inference speed has become a more critical battleground than training scale, and Cerebras' giant chip holds an overwhelming advantage in this specific niche. Concurrently, the company demonstrates the performance advantages of its hardware components by operating its own data centers and building a stable recurring revenue stream. This high-profile strategic cooperation agreement with OpenAI not only validates its technical prowess but also positions Cerebras to potentially share in the hundreds of billions of dollars being invested globally in new AI computing infrastructure.
Cerebras founder and CEO Andrew Feldman emphasized that the AI inference phase—the process where models respond to queries—is critically important for technological advancement, and this is precisely where his product's core competitiveness lies. In an interview, Feldman further elaborated, "We chose an innovative and differentiated architecture design not only because it enables faster response times but also because it creates significant value for our partners. This strategic collaboration with leading institutions like OpenAI not only propels Cerebras into the industry's top tier but also marks the official entry of high-speed inference technology into the mainstream application stage."
It is noteworthy that Cerebras is currently actively advancing its financing process in preparation for a potential initial public offering (IPO). According to people familiar with the matter earlier this week, the company has begun discussions regarding a new funding round of approximately $1 billion. These sources also indicated that, prior to the completion of this round, the startup's valuation has already reached around $22 billion.
OpenAI's $10 Billion Compute Order Shakes the Market: Cerebras' Technological Breakthrough Challenges NVIDIA's Throne For OpenAI, the agreement with Cerebras is merely the latest in a series of large-scale data center deals aimed at expanding its computing capacity, profoundly reflecting an unprecedented strategic consensus within the tech industry—that demand for high-power-consumption AI tools will maintain strong long-term growth.
Looking at specific industry dynamics: Last September, NVIDIA announced a strategic investment of up to $100 billion in OpenAI, specifically earmarked for building AI infrastructure and new data centers with a power capacity of at least 10 gigawatts; in October, AMD disclosed plans to deploy graphics processing units for OpenAI equivalent to 6 gigawatts over multiple years. Notably, a power capacity of 1 gigawatt is roughly equivalent to the output of a conventional nuclear power plant. Simultaneously, OpenAI, the developer of AI tools like ChatGPT, is collaborating with Broadcom to develop its own proprietary chips, further strengthening its technological autonomy.
According to the joint statement, Cerebras and OpenAI have been exploring technical collaboration paths continuously since 2017. This cooperation has yielded significant results recently, with Cerebras demonstrating a breakthrough advantage in supporting OpenAI's GPT-OSS-120B model—achieving processing speeds 15 times faster than traditional hardware, fully validating the performance potential of wafer-scale architecture in ultra-large model inference scenarios.
While the agreement showcases the growth vitality of the AI industry, its astronomical contract value has further intensified market anxiety over the capital expenditure pressures facing tech giants. Investors are increasingly concerned that AI infrastructure investment has evolved into a "profit black hole," where massive, sustained spending could severely erode the profit margins of large technology companies over the coming years, casting doubt on the sector's overall earnings outlook and triggering sell-offs.
Furthermore, this massive $10 billion deal also signals a loosening of the monopolistic structure in the AI compute market, having a profound impact on the valuation logic of existing giants. Cerebras, leveraging the technical superiority of its "wafer-scale chip" in inference speed, has secured a strategic endorsement from a top-tier AI institution, directly challenging NVIDIA's absolute dominance in the high-performance AI chip arena. The market is conducting a fresh assessment of the long-term premium potential for NVIDIA and its ecosystem partners. Intertwined expectations of intensified competition and concerns about capital efficiency contributed to the corrective trend seen in technology stocks during the January 14 trading session.
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