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This article centers on Cerebras Systems' alternative hardware architecture designed to overcome physical limits in scaling large AI models, making it a core discussion of AI infrastructure constraints rather than an incidental mention. The piece details how wafer-scale computing addresses the compute and energy demands required for next-generation AI workloads.
Cerebras Systems’ Andrew Feldman on whether AI can keep scaling at TechCrunch Disrupt 2026
At TechCrunch Disrupt 2026, Cerebras Systems CEO and co-founder Andrew Feldman will explore the growing demand for compute, energy, and infrastructure, how Cerebras is approaching those constraints differently, and what comes next if today’s AI hardware reaches its limits.
AI models keep getting more capable. But every leap forward demands more compute, energy, and infrastructure. How far can that continue? Cerebras Systems has spent the past decade challenging a basic assumption behind AI computing: that increasingly powerful AI must depend on conventional chip architectures. The company built its approach around wafer-scale computing and today provides AI compute through on-premise systems and its cloud platform. At TechCrunch Disrupt 2026, Cerebras…
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