In July 2026, the median US business on Ramp's platform spent $11.95 per employee per month on AI. The top 1% of spenders: $7,400. That is a 619x gap inside the same economy, measured on the same card platform, in the same month.
Within four days, that single number had been spun into three contradictory headlines by three credible parties. Following how that happened tells you more about where AI adoption actually stands than any one of the headlines does.
The ladder
Ramp's July data has more in it than the ladder. Anthropic is now billed by 43.5% of US businesses on the platform, OpenAI by 39.7% with growth slowing, xAI by 4% and climbing fastest. And Ramp's own headline for the report was not about the gap at all. It was "Cracks in the AI thesis": their economist reads the data as businesses finding "a new upper bound for how much businesses are willing to spend on AI."
One dataset, three narratives
Watch the telephone game, dated to the day. August 12: Ramp publishes the index under a spending-ceiling headline. August 14: a16z re-charts the same numbers as a "wild adoption gap," and partner Olivia Moore frames it as the story of the year:
Moore
"The median company is spending $12 / employee / month on AI. The top 1% are spending $7,500 / employee / month. Not sure we've ever seen an adoption gap quite like this."
August 15: Aaron Levie reads the same chart as proof that "AI spend is nowhere close to hitting any walls," with the top 10%'s behavior today previewing the median's behavior in three years. Greg Brockman replies to the 600x chart: "sounds accurate." August 18: Merge launches "Merge for Workforce" straight into the anxiety, claiming a 75% AI spend cut "in one click" via device-level model routing policies:
Ding
"We just cut AI spend by 75% across an entire workforce in one click... Without Merge, your employees are left choosing one of two options: 1. Spend 100x the cost they should be from using frontier models for everything 2. Deliver subpar work"
Read the fine print on that one: Merge's own blog headline says "cut token spend in half," not 75%, and no methodology or named customer backs either number. The product itself is real and sensibly boring: an MDM-deployed client that inventories the AI tools employees actually installed, then enforces which models and connectors each team's harnesses start with. Routing policy as IT infrastructure. Coinbase built this in-house in June; now it is a vendor category.
What the gap actually is
Ceiling, gap, or no walls: which is it? Held together with what this playbook has tracked since June, the readings collapse into one. Token prices fell ~99% in three years while bills went up, because usage is the variable that matters. The $12 median is not a company using AI efficiently; it is a company whose employees have a ChatGPT seat and nothing wired into the work. The $7,400 top percentile is companies where token spend became a payroll-shaped line item, sometimes badly managed, but structurally committed. The 619x gap is not an adoption curve lagging. It is two different activities sharing the word "AI."
The strategic read for a founder or CEO sits in the middle rung: $650 per employee per month, the top decile, is roughly the cost of one junior hire spread across a 20-person team. That is the tier where deployed agents, routed models, and measured savings live. The median tier is a rounding error because nothing real is running. The top tier is a war budget. The decile tier is a decision.
$12 is a seat. $650 is a strategy.
Which rung are you on?
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