AI Spending Concentrates in Top 10% of Customers
Adoption counts overstate how broad AI monetisation really is: revenue tracks machine workload per customer, not seat licences. Investors valuing AI infrastructure on customer numbers risk misreading demand, so per-customer inference spend is the metric to watch. If continuous agents spread beyond the top decile, infrastructure revenue could grow much faster than the customer count.
Torsten Slok, Chief Economist at Apollo, reports that the top 10% of AI customers account for 99.
5% of model-serving spend and 99% of neocloud spend.The average firm in that top group spends roughly 1,791x more on inference, and about 891x more on neoclouds.
By comparison, the gap is only about 101x for non-AI SaaS and 48x for CRM.This contrasts with normal enterprise software, which scales mostly with seats, while AI scales with machine work and inference volume.
AI adoption is spreading but AI spending remains concentrated.
"The top 10% of customers (in AI) account for 99.5% of model-serving spend and 99% of neocloud spend, leaving the bottom 90% of firms with 0.5% and 1%."
- Torsten Slok, Chief Economist/Partner of Apollo
The average firm in that top group is spending roughly 1,791x more on inference. For neoclouds, the gap is about 891x. The same calculation is only about 101x for non-AI SaaS and 48x for CRM. Apollo Academy
That is a very different economic shape from normal enterprise software. Software scales mostly with seats.
AI scales with machine work. 2 companies can both count as "AI adopters," while 1 runs a chatbot a few hundred times a month and the other has agents making millions of model calls every day. They look identical in an adoption chart and completely different in infrastructure revenue.
So counting AI customers can now be badly misleading.
So the next big unlock should turning today's huge low-spend tail into persistent machine workloads.
If ordinary companies start running agents continuously across support, coding, sales, operations and back-office work, infrastructure demand can grow far faster than the number of AI customers, because the real unit of growth is no longer the customer in the AI era.
It is the amount of work the machines are doing.
This report is categorized under AITOP's "Industry" desk, focusing on US frontier AI labs and Silicon Valley technology leaders. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include AI spending, neoclouds, inference, Apollo.
Source attribution: curated from rohanpaul_ai (@rohanpaul_ai).
