Spending on artificial intelligence by US businesses reached a new high in October 2026, but the pace of growth slowed significantly, raising questions about how broadly companies are adopting paid AI services. According to the November Ramp AI Index, corporate AI spending increased just 1.65% month over month. The figures suggest that although demand for AI tools continues to expand, spending remains concentrated among a relatively small group of businesses rather than spreading evenly across the corporate sector.
The data, published on November 11 by financial technology company Ramp, is based on payment activity from more than 85,000 US businesses. It reveals a substantial gap between the companies spending heavily on AI and the typical business. The top 1% of companies by AI spending recorded median expenditure of $10,200 per employee per month, while the median company spent just $16.32 per employee. The index also shows a changing pattern in enterprise adoption, with customer-support teams increasing their AI spending while software development remains the largest spending category. <Cite refs={[“turn619485search0″,”turn619485search1”]}/>
US Corporate AI Spending Reaches a Record High
The Ramp AI Index tracks business spending on artificial intelligence products and services using payment data from companies on its financial platform.
Its October findings show that corporate AI spending increased 1.65% compared with September, reaching a new record.
However, the modest monthly increase indicates that the expansion of paid AI adoption may be losing momentum compared with earlier periods.
The difference between the largest AI spenders and the typical business is particularly striking.
| Business spending group | Median AI spending per employee per month |
|---|---|
| Top 1% of companies | $10,200 |
| Top 10% of companies | $801 |
| Median company | $16.32 |
The top 1% increased their median spending by approximately 9% during October, while the typical company’s spending increased by just $0.93.
This suggests that companies with substantial AI budgets are continuing to invest aggressively, while many other businesses remain at an early stage of adoption.
The figures also demonstrate why record corporate AI spending should not automatically be interpreted as widespread adoption across the entire US economy. <Cite refs={[“turn619485search0″,”turn619485search1”]}/>
Just 1% of Companies Drive 80% of OpenAI and Anthropic Revenue
One of the most important findings in the report is the concentration of AI spending among a small number of businesses.
According to Ramp chief economist Ara Kharazian, approximately 1% of companies account for 80% of the revenue generated by OpenAI and Anthropic from the businesses covered by the analysis.
These high-spending customers are largely venture-backed companies operating in technology, finance and technical services.
This concentration creates a potential challenge for AI companies.
Although large enterprise customers can generate substantial revenue, relying heavily on a small group of businesses makes growth more dependent on their continued spending decisions.
If these companies reduce their AI budgets, delay projects or shift towards cheaper models, revenue growth could become more difficult to sustain.
The findings also suggest that AI adoption remains uneven across industries. Outside the sectors with the strongest technology and financial-services exposure, paid adoption appears considerably less developed.
For AI providers, expanding beyond early adopters and attracting ordinary businesses could become increasingly important for long-term growth. <Cite refs={[“turn619485search0″,”turn619485search1”]}/>
OpenAI Spending Grows Faster Than Anthropic in October
The Ramp data also provides a comparison of spending trends for major AI providers.
Business customers’ spending on OpenAI increased 2.1% month over month in October, while spending on Anthropic increased 0.5%.
The figures indicate that OpenAI experienced faster monthly spending growth among Ramp’s business customers during the period.
However, the results do not necessarily represent the overall market shares or total revenue of either company.
Ramp also reported changes in adoption rates:
- Anthropic’s adoption reached 44.8%, increasing 1.1 percentage points.
- OpenAI’s adoption remained relatively stable at 40.6%.
- Overall adoption of AI services increased to 56.8%.
- SpaceXAI recorded adoption of 6.3% within Ramp’s dataset.
The figures measure adoption among businesses represented in Ramp’s data, not the percentage of all US companies using each provider.
The data also indicates that competition is expanding beyond the two largest providers, potentially creating additional pressure on pricing and customer retention. <Cite refs={[“turn619485search0″,”turn619485search1”]}/>
AI Model Prices Fall 21% as Usage Continues to Increase
Another important trend highlighted in the October index is the declining cost of AI processing.
Ramp reported that the average price per million processed text tokens fell 21% during the month.
Tokens are the small units of text that AI models process when users submit prompts and receive responses. The cost of processing these tokens is an important part of the operating expenses for businesses that use AI through application programming interfaces.
Falling prices mean companies can process more information without increasing their budgets at the same rate.
This creates an important distinction between AI usage and AI spending.
Businesses may increase the number of prompts, documents and tasks processed by AI systems while their total expenditure grows more slowly because the cost of each unit of processing is declining.
Competition between OpenAI and Anthropic, along with the availability of open-weight models developed in China, is contributing to pricing pressure.
For AI companies, lower prices can encourage adoption but may also reduce revenue per unit of usage.
The long-term financial impact will depend on whether increased usage can compensate for declining prices. <Cite refs={[“turn619485search0″,”turn619485search1”]}/>
Software Development Still Accounts for 72% of AI Spending
Software development teams remain the largest source of tracked enterprise AI spending.
According to Ramp’s departmental analysis, engineering teams accounted for 72% of classified AI spending in October.
Coding assistants and AI-powered development tools have become important business applications because they can help programmers write code, identify errors, generate tests and understand complex software projects.
However, spending by engineering departments has declined from its summer highs, suggesting that this category is no longer accelerating at the same pace as earlier in the year.
The shift is important because software development has been one of the strongest commercial use cases for generative AI.
Companies are now exploring additional applications that could expand AI spending beyond technical teams.
Customer Support AI Spending Rises Sharply
Customer support and customer success departments recorded the strongest growth in their share of tracked AI spending.
Their share increased from just 0.8% in June to 13.3% in October.
The increase may reflect growing adoption of AI-powered customer-service chatbots and automated support systems.
Businesses can use these tools to answer common customer questions, classify incoming requests, retrieve information and direct complicated cases to human employees.
Unlike software development, customer support offers opportunities to deploy AI across large numbers of customer interactions.
However, the category still has considerable room to grow, and Ramp’s economist noted that its expansion has not yet matched the rapid growth previously seen in engineering teams.
Customer-service applications may also favour faster and cheaper AI models rather than the most expensive flagship systems. This could increase usage without generating the same revenue per task for AI providers. <Cite refs={[“turn619485search0″,”turn619485search1”]}/>
Why the Findings Matter for OpenAI, Anthropic and the AI Industry
The October figures highlight a growing challenge for companies building commercial AI products.
The industry has attracted enormous investment based on expectations that businesses will increasingly pay for AI tools and integrate them into everyday operations.
However, the Ramp data suggests that enterprise spending is still concentrated among a limited group of customers.
For OpenAI and Anthropic, the next phase of growth may depend on convincing more traditional businesses to pay for AI services.
The expansion of customer-support applications could provide one opportunity, particularly as companies seek to reduce repetitive workloads and improve response times.
At the same time, falling model prices could make it harder for providers to increase revenue simply by processing more tokens.
The companies will need to balance lower costs with higher usage, stronger enterprise relationships and more valuable AI products.
Important Limitations of the Ramp AI Index
Although the index provides a useful snapshot of business spending, it does not represent every US company.
Ramp’s dataset covers more than 85,000 businesses using its financial services. Its customer base is weighted towards growth-oriented and technology-friendly companies, which may have higher AI adoption than the broader business population.
The index may also miss spending on free AI tools and services accessed through employees’ personal accounts.
Additionally, some of the token-spending figures come from a smaller group of companies using Ramp’s Token Spend Management product. Those figures should not be treated as representative of all businesses in the index.
Consequently, the findings are best understood as an indicator of spending patterns within Ramp’s customer base rather than a comprehensive measurement of total US corporate AI expenditure. <Cite refs={[“turn619485search0″,”turn619485search1”]}/>
The Bigger Picture
The October data suggests that the next stage of enterprise AI adoption may be more complicated than the industry’s early growth figures indicated. While leading technology companies and venture-backed businesses continue spending heavily, the typical company’s AI budget remains comparatively small. This creates a challenge for providers that need to expand beyond early adopters and demonstrate clear financial returns from AI investments.
At the same time, falling model prices and the emergence of new applications in customer support could change the economics of the industry. Lower processing costs may encourage wider adoption, but they also put pressure on revenue per unit of usage. The companies that succeed may be those that make AI useful across multiple departments while delivering measurable productivity improvements at a sustainable cost.
Looking Ahead
The coming months will show whether AI spending begins to spread more evenly across US businesses or remains concentrated among a small group of heavy users. Customer support, business operations and other non-engineering departments could become important sources of additional demand as companies explore practical applications beyond coding. OpenAI, Anthropic and competing providers will also face pressure to demonstrate that their products can deliver measurable business value while adapting to declining model prices.
For investors and technology companies, the key indicator will not simply be whether AI spending reaches another record. It will be whether more businesses begin paying for AI services, whether usage translates into sustainable revenue and whether companies can justify their investment through improved productivity. October’s figures show that the market is still expanding, but the pace and distribution of that growth deserve closer attention.
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