Compute is the cheapest part of an AI deployment. That is now true across almost every workload that matters to an operating company. The expensive part — the part nobody puts on the slide — is the human org chart that catches the failure modes.
Most cost overruns we see in mid-market AI rollouts come from two places: a customer-support load that doubled when the model started confidently giving wrong answers, and a QA function that nobody budgeted for because the demo did not have one.
The throughput-quality gap
Throughput improves before quality does. That gap is where money disappears. Plan for it explicitly: budget for a quality function before the rollout, not in response to the first complaint thread.
"Inference is cheap. The org chart that catches the failure modes is not."
The teams getting AI economics right are the ones who treat the model as the cheap ingredient and the operating workflow around it as the expensive one. Most pitch decks have this exactly backwards.
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