Artificial intelligence is rapidly becoming one of the biggest areas of corporate investment in the United States, with spending reaching unprecedented levels among the country’s biggest AI adopters.
A new report by Ramp’s latest AI Index, shows that the top 1% of U.S. firms spent a median of $7,400 per employee per month on AI tools and infrastructure in July 2026. That figure is more than 600 times higher than the median company’s outlay of roughly $12 per employee.
This highlights just how aggressively businesses are deploying the technology to gain a competitive edge. The gap underscores a deepening divide in corporate AI adoption. Firms in the top 10% spent about $650 per employee per month, still a fraction of what the leading cohort invests.
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These expenditures cover large language model subscriptions, coding agents, API tokens, and GPU cloud computing. Ramp draws the data from anonymized transaction records of tens of thousands of US businesses that use its corporate card and expense platform.
The acceleration has been rapid. In early 2024, reports revealed that the top 1% was spending less than $1,000 per employee per month. Spending has more than tripled across the distribution in recent months, yet the bulk of the growth remains concentrated at the high end.
Much of the spending is being led by the largest technology companies, particularly Microsoft, Amazon, Alphabet, Meta and Oracle. Analysts describe the pattern as “whales-first,” in which a small number of aggressive adopters account for most of the overall increase in AI expenditure.
These companies are directing enormous amounts of capital toward the physical infrastructure required to develop and operate increasingly sophisticated AI systems. Their investments include massive data centers, AI accelerators, networking equipment, electricity infrastructure, and cloud-computing capacity.
In 2026, the investment boom has moved well beyond experimentation. Goldman Sachs estimates that AI-related investment in the United States could reach about $600 billion this year, equivalent to roughly 2% of U.S. GDP and 10% of business fixed investment.
This spending suggests that the AI race is moving beyond the question of whether employees have access to AI. Companies are increasingly competing over how deeply AI can be embedded into employees’ daily work.
Some businesses are using AI to help employees write documents, analyze information, generate software, conduct research, and communicate with customers. Others are deploying AI agents capable of performing multiple steps in a workflow with considerably less human intervention.
The trend is also visible among major financial institutions. Citi, for example, has trained about 4,000 employees as AI stewards and has reported that nearly 90% of its workforce uses AI tools.
JPMorgan has deployed its proprietary generative AI platform to more than 200,000 employees, while Wells Fargo has introduced AI tools designed to increase the productivity of financial advisers.
The deployment of capital is not limited to employee spending. Companies are also redirecting existing resources toward AI development. Businesses are reallocating portions of their software and labor budgets to fund AI initiatives, while hiring or acquiring specialized talent in areas such as machine learning, data science, semiconductor engineering, and AI research.
However, the disparity in spending by top companies, raises questions about competitive dynamics. Companies that can sustain multi-thousand-dollar monthly AI costs per worker are building capabilities far beyond those of firms that treat AI as a modest software expense.
Whether this polarization continues or begins to narrow will shape how widely the productivity gains from AI spread across the American economy in the years ahead.
Outlook
Looking ahead, AI spending by U.S. companies is likely to continue rising as businesses move from experimentation to deeper integration of AI into their core operations.
The most aggressive adopters are expected to increase spending on AI agents, advanced models, computing infrastructure, and specialized software as they seek to automate more complex tasks and improve employee productivity.
The next phase of the AI investment cycle could therefore shift from simply acquiring more AI tools to determining which deployments deliver measurable business value.
If the technology continues to produce significant productivity gains, corporate AI spending could expand further and become a permanent component of employee and infrastructure budgets.