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Apollo reports top 10% of AI customers account for nearly all spending
New data from Apollo's chief economist reveals extreme concentration risk in AI infrastructure, with a tiny sliver of clients driving virtually every dollar of model-serving and neocloud revenue.
The AI boom has a customer concentration problem that makes even traditional software look diversified. According to a new analysis from Apollo Global Management’s Chief Economist Torsten Slok, the top 10% of AI customers are responsible for 99.5% of model-serving spend and 99% of neocloud spend.
The numbers behind the imbalance
Slok’s analysis, published on September 24, draws on data from Ramp’s spend-management platform, which tracks corporate purchasing behavior across thousands of businesses.
On the adoption side, roughly 10% of software-spending businesses on Ramp now engage a GPU vendor, up from under 4% just two years ago. Model-serving and inference participation jumped from 2.4% to 8.7% of firms over the same period. Neocloud utilization climbed from 2.0% to 3.3%.
The title of Slok’s report captures the paradox neatly: “AI Adoption Is Spreading. AI Spending Is Concentrating.”
For context, consider how this compares to other enterprise software categories. Non-AI SaaS solutions show the top 10% of firms accounting for 91.8% of expenditure. CRM software is even more distributed, with the top decile responsible for 84.2% of spend. At 99.5%, AI model-serving spend isn’t just concentrated ā it’s essentially a bilateral market wearing the costume of a broad-based industry.
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What’s driving the concentration
The Ramp data suggests that rather than investing in owned hardware, most firms are opting for cancellable software subscriptions for AI technologies. Companies treating AI compute as an operating expense rather than a capital investment can scale down quickly, with no sunk costs keeping them locked in.
Why this matters for the AI supply chain
For investors evaluating AI infrastructure companies, these findings suggest that top-line revenue growth may be masking underlying customer concentration risks. A company can report strong quarterly numbers while remaining dangerously dependent on purchase decisions made by a dozen procurement teams.
The gap between AI adoption curves and AI spending curves is one of the more important data points for anyone trying to separate the durable economics of the AI buildout from the narrative surrounding it.