Ramp data shows business AI token spend barely grew in August and September while usage kept rising

Businesses are using more AI but have stopped spending much more on it, according to Ramp spend data reported by TechCrunch on November 6. Token spend grew 1.5% across August and September, compared with 42% growth in July from the previous month. October data showed a re-acceleration, with token spend up 8.9% from September 23 to October 23. "Almost the entire spend on AI is from venture-backed, high-growth tech companies," Ramp's lead economist Ara Kharazian told TechCrunch.

Who is doing the spending

Median firms spend $12 to $15 per employee per month, and the top 10% spend $600 to $1,000, TechCrunch reported from Ramp's data. In Ramp's October AI Index, published October 14, Kharazian wrote that 80% of spend on OpenAI and Anthropic is driven by 1% of firms, and that the top 1% spent $9,568 per employee per month in September, down 3.1% from its July high of $9,873. He told TechCrunch that while there is demand for coding agents in software and finance, "it's not necessarily true that a doctor's office or a car dealership will need to spend that much on AI."

Why spend is flattening

Kharazian said the slowdown is not a demand problem, since volumes are up, and pointed to competition among model vendors: "OpenAI and Anthropic price cuts have…just made it cheaper to use AI." He also said experienced buyers get better at limiting their own bills. "You're more likely to use defaults that prevent usage of very expensive models for long-running tasks. You're more likely to impose some budgets on employees. You're more likely to offload some of the spend from standard and light models to even cheaper light models via routers," he said. In the October index he wrote that token volumes rose over the same period, and that in his view the main driver is price declines at the frontier along with efficiency and quality improvements across models.

Why it matters

A flat AI bill alongside rising token volume means each token is getting cheaper, which says nothing about whether the extra volume is useful. A company judging its AI spend needs to see what that growing volume actually produced, by team and by tool, before it counts lower unit prices as a gain.