Enough technical depth for a CTO. Enough plain language for a CFO. The shared vocabulary your leadership team needs before any AI investment conversation.
Compute is the processing capacity used to run AI workloads. It can come from GPUs, TPUs, custom AI chips, cloud APIs, hosted models, or internal clusters. Compute powers both model creation (training, fine-tuning) and model usage (inference, agentic workflows).
Training creates or improves models. Inference runs models to generate outputs. Fine-tuning adapts existing models. Agentic workloads use repeated inference plus tools, memory, and workflows. Internal acceleration uses AI to help the company itself build faster. Each consumes compute differently.
A token is a unit of input or output. A useful output is a business event: a resolved ticket, an accepted code change, a qualified lead, a reviewed contract. The most important shift ROC asks you to make is to measure cost per useful output, not cost per token.
Two organizations can spend the same on compute and get wildly different returns. Effective compute = raw compute × utilization × fungibility × reliability × price-performance × model efficiency. ROC's diagnostic isolates which factor is your bottleneck.
Compute → useful intelligence → business value → revenue and productivity → capital → more compute. Organizations that close this loop deliberately get a compounding advantage. Organizations that don't end up funding compute as pure cost of goods sold.