Everyone is watching the AI software layer — the models, the chatbots, the agents. The smarter watch is on the hardware underneath all of it. Cerebras Systems went public during one of the most unprecedented IPO seasons in recent memory, and its core product — a wafer-scale chip built specifically for AI workloads — sits at the center of a race that will determine which companies can train and run AI at the scale the next decade requires. The shift from training to inference is the technical detail that matters most right now: training is what it costs to build a model, inference is what it costs every single time someone uses it. At billions of queries per day, inference efficiency is where the real money is being made and lost — and chip architecture is the deciding factor.

⏱️ 00:00 — Cerebras IPO and AI Hardware Race

📊 Key stats from this segment:

- Cerebras Systems built the world's largest chip — the Wafer Scale Engine — designed to eliminate the memory bandwidth bottlenecks that slow AI training on traditional GPU clusters (Cerebras Systems)

- NVIDIA controls an estimated 70–95% of the AI training chip market; Cerebras, AMD, and custom silicon from Google (TPUs) and Amazon (Trainium) are the primary challengers (Morgan Stanley / Bloomberg, 2025–2026)

- AI inference spending is projected to surpass AI training spending by 2026 as deployed models scale to billions of daily queries — inference efficiency is now the primary competitive frontier (Goldman Sachs AI Infrastructure Report, 2025)

- Global AI chip market projected to reach $311 billion by 2029, growing at a compound annual rate of approximately 29% (IDC / MarketsandMarkets, 2025)

- IPO valuations for AI hardware and infrastructure companies in early 2026 reached multiples not seen since the 2021 tech boom — driven by hyperscaler demand for compute at scale (Renaissance Capital / Bloomberg, 2026)

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