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The adoption of AI tools by businesses slowed in August, according to spending data at 70,000 companies collected by the payments company Ramp. The latest survey<\/a> shows 56% of Ramp customers paid for AI products in August, rising just 0.4% from the month before.<\/p>\n This isn\u2019t the first time Ramp\u2019s metrics have shown adoption slowing down. Last year, the company\u2019s AI index showed little to no growth in adoption between August and October, only to have growth pick up again as the year finished. <\/p>\n Still, the extreme pace of the AI buildout means even small slowdowns can be cause for concern. The gobsmacking investment in AI infrastructure by frontier labs and hyperscalers rests on the hope that there is plenty of revenue<\/a> out there to pay it back. Thus far, usage has grown steeply, particularly as software engineers adopted agentic coding tools \u2014 but if that adoption slows down, revenue is likely to slow as well.<\/p>\n Ramp\u2019s figures may overstate overall adoption, thanks to the company\u2019s techy clientele: An ongoing US Census Bureau survey of AI adoption<\/a> updated on August 23 shows just 22% of businesses report using AI. Ramp\u2019s survey isn\u2019t necessarily representative of the market, but it\u2019s one of the few direct spending data sets available and potentially a leading indicator.<\/p>\n To be sure, this data is from August, when much of the industry is on vacation. That may explain the doldrums. But there are other warning signs for companies that depend on token spend, per Ramp economist Ara Kharazian.<\/p>\n First, a major decline in AI spend per employee in the top 1% of firms in his sample, falling nearly 10% to $7,205. That may be the vacation-token factor, but it also speaks to falling token costs. As OpenAI and Anthropic have cut prices, average token costs have declined to $0.68 per million tokens, as opposed to the 2026 peak of $1.15 per million tokens in March.<\/p>\n The data suggests that the labs have yet to make up for the price cuts with growing volume. And the same incentives have many customers choosing to use older, cheaper models like OpenAI\u2019s ChatGPT 5.6-Terra and Anthropic\u2019s Sonnet instead of the more powerful frontier releases. Employees at frontier labs have said much of the cost of training is recouped in the first weeks of a new model\u2019s release, and slower adoption could threaten that dynamic. <\/p>\n Still, for all the talk of open-weight models threatening the frontier labs, only 6.4% of AI-spending businesses used model-serving or inference platforms in August; a share that\u2019s growing steadily but not fast enough to drive the dynamics of broader business adoption. <\/p>\n \u201cWe are showing that competition between OpenAI and Anthropic is making AI more accessible, and also driving the price down for companies\u2014and not just driving the price down, but driving spend down at the top 1% of companies that previously the market was expecting to drive much of the growth going forward,\u201d Kharazian said.<\/p>\n That also helps explain the focus at AI labs on winning over non-technical users<\/a> for AI co-working tools. <\/p>\n This data point \u2014 dare we call it a blip? \u2014 could be a bad sign if you\u2019re a model-builder or a hyperscaler with a couple hundred billion of chips on order. But, Kharazian notes, \u201cit depends on who you are in the market. If your company is using AI, it\u2019s great.\u201d<\/p>\n<\/div>\n When you purchase through links in our articles, we may earn a small commission<\/a>. This doesn\u2019t affect our editorial independence.<\/em><\/p>\n