AI models continue to grow in capability, but each advancement requires significantly more computing power, energy, and infrastructure. How far can this trajectory continue?
For over a decade, Cerebras Systems has challenged the industry-standard assumption that powerful AI must rely on conventional chip architectures. The company built its approach around wafer-scale computing, offering AI compute through both on-premise systems and its own cloud platform.
To understand what could determine the future of AI scaling, secure a ticket to Disrupt to hear from a founder solving these infrastructure challenges. Bring a co-founder, colleague, or peer for 50% off.
Andrew Feldman co-founded Cerebras in 2015 after years of building companies around computing infrastructure. Before Cerebras, he co-founded and led SeaMicro, an energy-efficient microserver startup acquired by AMD in 2012, and held leadership roles at Force10 Networks and Riverstone Networks.
At Cerebras, Feldman and his co-founders took on a problem previously considered impractical: bringing wafer-scale computing to market. Rather than cutting a silicon wafer into individual chips, Cerebras developed a processor built on the wafer itself, an architecture designed specifically for demanding AI workloads.
Now Cerebras is scaling this approach as demand for AI compute accelerates. The company raised $5.5 billion in its May IPO and signed a multiyear agreement with OpenAI to deploy 750 megawatts of Cerebras systems from 2026 through 2028. In August, the company introduced CS-4, the latest generation of its wafer-scale AI infrastructure.
Can approaches like Cerebras' deliver the compute increasingly powerful AI demands? Secure a Disrupt pass for half price to hear from an entrepreneur who has spent more than a decade betting on a different way to build AI hardware.
More powerful processors alone do not solve the scaling problem; they require supporting data centers, electricity, cooling, and manufacturing capacity.
Cerebras is already confronting this challenge. In August, the company reported more than 600 megawatts of data center capacity live or under contract for delivery by the end of 2027 and stated it was increasing manufacturing capacity more than tenfold during 2026. It also plans to bring its first European data center capacity online this year and expand to 200 megawatts there by the end of 2027.
Scaling AI is not solely about designing faster processors; it requires enough physical infrastructure to put that compute to work.
For founders, investors, and technology leaders, Feldman can put these constraints into context regarding where compute demand is heading and the limits of today’s hardware.
Cerebras pursued wafer-scale computing long before today’s AI infrastructure boom and is now scaling its computing and manufacturing capacity as demand for AI accelerates.
At Disrupt, Feldman will explore what growing demand for compute, energy, and infrastructure means for AI’s future and what could happen if conventional hardware can no longer keep pace. His session is one of 200+ sessions across six industry stages, roundtables, and breakouts at Disrupt, taking place October 13-15 at Moscone West in San Francisco.
Register for Disrupt to learn how long AI can keep scaling and what must change for it to continue. Bring a co-founder, partner, or peer for 50% off.
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