The Biggest Chip Ever Built — Why OpenAI Runs On It | Cerebras CEO Andrew Feldman
About this episode
Matt Turck interviews Andrew Feldman about the significance of fast inference in AI and the role of Cerebras in building advanced AI chips.
AI is no longer just a race to train smarter models. As AI moves into production, the bottleneck is increasingly inference: how fast models can generate tokens, use tools, reason, verify, and act. In this episode of the MAD Podcast, Matt Turck sits down with Andrew Feldman, co-founder and CEO of Cerebras, to explain why fast inference may define the next era of AI. Cerebras is known for building a chip the size of a silicon wafer. But this conversation is not just about one company or one chip. It is a deep dive into the AI infrastructure stack: GPUs, ASICs, memory, HBM, SRAM, data centers, power, TSMC, AWS, OpenAI, agents, reasoning models, and why speed changes what AI products can become. Andrew explains why “tokens per second per user” matters, why generating a single word can require moving the equivalent of 100 HD movies through memory, why agents amplify latency, why GPUs struggle with certain inference workloads, and why fast AI may eventually reshape SaaS itself. This is a reference conversation on fast inference, AI chips, and the next compute bottleneck. (00:00) Cold open & Intro (01:31) Why speed became the AI bottleneck (02:32) Tokens per second per user…
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