As data centers evolve into AI factories, compute has shifted from a cost center to a revenue driver. “Compute is revenue,” said Jensen Huang, co-founder and CEO of NVIDIA. “Without compute, there is no way to generate tokens. Without tokens, there’s no way to generate revenue. So, in this new world of AI, compute equals revenue.” This reframe changes an organizations’ calculus. If compute is revenue, what do you optimize for? Here are 5 questions to consider: Are you measuring what actually drives AI factory revenue? Most AI factories are power-constrained, so tokens per watt dictate how much revenue you can generate and the cost per token impacts the AI factory profit margin. But neither of these metrics should be evaluated at a single operating point. Batch jobs, real-time chat, and agentic workloads demand different points on the throughput-latency curve. AI chips that perform well at only a few points will underserve the full range of workloads. Additional key operational metrics like time to first token (TTFT), mean time between interruptions (MTBI), and platform useful life are the bedrock of AI factory efficiency. They dictate how quickly an AI factory comes online to generate tokens, the reliability of its revenue streams, and its long-term ability to remain productive as AI workloads evolve. How does agentic AI change what your CPU needs to deliver? Data center CPUs have historically been optimized for parallel throughput, where more cores improve aggregate capacit…

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