10/01/2026
Understanding the U.S. Federal Government’s AI Spending
https://www.csis.org/analysis/understanding-us-federal-governments-ai-spending
The U.S. government is buying more artificial intelligence now than ever before. Between fiscal year 2019 and FY 2025, federal agencies awarded 2,255 identified AI-related awards, with $4.1 billion in total obligations. The number of AI-related contracts more than tripled over this period, and annual obligations also increased significantly, reaching $1.162 billion in FY 2025. This increasing demand is dominated by the Department of Defense (DOD), which accounts for 83 percent of obligations. The CSIS Futures Lab followed the federal money trail to understand how the U.S. government buys artificial intelligence, drawing on two sources: solicitation notices posted on SAM.gov and awarded federal contracts recorded in USAspending.
Q1: Is federal demand for AI increasing?
A1: Yes. Federal demand for AI has increased substantially since FY 2019 and continues to grow. Figure 1 shows that the number of awarded contracts mentioning artificial intelligence more than tripled between FY 2019 and FY 2025. SAM.gov solicitation notices also increased, with FY 2025 reaching 71 AI-related notices.
Figure 1: Federal AI Procurement Activity, FY 2019–2025
In terms of total obligations, annual federal AI obligations rose sharply, reaching $1.162 billion in FY 2025. Among these contracts, generative AI is also becoming increasingly visible. Following ChatGPT’s launch in November 2022, contracts tagged as generative AI climbed to 56 in FY 2024 and 118 in FY 2025 (see Figure 2).
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Figure 2: Generative AI and LLM–Tagged Federal Contract
Annual award observations
Future demand and federal spending on AI are likely to continue to increase. Existing announcements show that the government will increase spending to modernize AI infrastructure. Data centers are a useful indicator, given the scale of investment flowing into this infrastructure. Federal AI demand and data center growth were correlated until the LLM revolution; however, since ChatGPT’s launch, data center growth has far outpaced federal AI contracting. This suggests that AI infrastructure investments are now mostly driven by commercial demand, and that federal spending is lagging behind the private sector on AI investments.
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Figure 3: Federal Contracts and Data Center Capacity
Index (2019 = 100)
Q2: Who is getting AI contracts?
A2: On the supply side, many suppliers participated while a substantial share of funding went to a few recipients. Across 2,255 AI-related contracts, 1,250 different vendors won awards (see Figure 4). Of those vendors, 66.2 percent won only one contract. At the same time, the three largest recipients accounted for 30.4 percent of net obligations, reflecting both the top-heavy and long-tailed nature of the market. The median award value is about $150,000 per year. Small businesses win most AI contracts by count (70 percent), but they hold a considerably smaller share of the total obligations (45 percent), meaning that they take many lower-value awards (see Figure 5).
This data suggests that the federal AI market remains relatively fragmented, while coalescing around a few heavyweights near the top. Most agencies are using many small and mid-sized awards. It is likely that the government is testing different tools, vendors, and use cases before committing to larger programs.
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Figure 4: Vendor Distribution by Number of Awards/Orders
Number of vendors
Figure 5: Business Size of Federal AI Contract Recipients
Share of annual award observations, FY 2019–2025
Large IT and consulting firms remain important within federal AI acquisitions. Traditional defense primes are less prominent than expected, accounting for just 2.8 percent of contracts and 5 percent of obligations. Some commercial AI firms have a larger dollar impact despite a small number of contracts: Palantir and Scale AI together account for $597 million, about 15 percent of obligations from less than 1 percent of contracts.
Q3: Which departments and agencies drive AI acquisition?
A3: The DOD is the central actor in federal AI acquisition, accounting for roughly 64 percent of AI contracts and 83 percent of obligations between FY 2019 and FY 2025 (see Figure 6). Civilian agencies such as the Department of Health and Human Services (HHS), the Department of Homeland Security, and the General Services Administration each account for much smaller shares. On the civilian side, HHS is the largest buyer by contract count (296 contracts, including 131 with the National Institutes of Health), followed by NASA (219 contracts), the Department of Transportation (136), the Department of Commerce (129), and Homeland Security (122).
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Figure 6: Agency Shares of Federal AI Contracting, FY 2019–2025
DODHHSNASACommerceDHSInteriorVAOther agencies
Within the DOD (Figure 7), the Air Force is the largest AI buyer in the awarded contract data, with 1,182 contracts and $872 million in obligations. The Defense Advanced Research Projects Agency also appears as a significant buyer, with 187 contracts worth $213 million. However, both are much less visible in the solicitation data used for this analysis. Furthermore, public data likely understates defense-related acquisition, of AI as some vehicles are less visible in open solicitation records. Moreover, AI spending within the intelligence community is likely not captured in the data presented here. This data should be considered as an indicative of a larger trend, as real spending numbers may be considerably higher.
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Figure 7: Defense Funding Organizations, FY 2019–2025
Q4: How is AI being governed?
A4: AI needs to be governed carefully, as it could have significant impact on national security. Models need careful evaluation before integration into government workflows; for example, many AI models show risky tendencies when performing key military tasks. Any acquisition process should detail key AI governance frameworks such as monitoring, data rights, benchmarking, model portability, and incident reporting.
However, according to the full solicitation language of AI-related notices for governance requirements, AI governance is uneven and limited. Overall, over half of AI solicitation notices in the sample contain no clear governance language. As shown in Figure 8, most AI-related contracts fall into two broad categories: “machine learning” and “general AI.” The “generative AI” category is growing quickly but remains a smaller share of total federal AI spending. Because many federal contract descriptions are short, the generative AI counts should be read as a lower-bound estimate.
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Figure 8: Federal AI Contracts by Technology Category, FY 2019–2025
Among the notices that do include governance language, most focus on performance measurement and process requirements, as can be seen in Figure 9. Benchmarking appears in over one-third of notices. These provisions can help agencies measure whether an AI system performs as promised, but they do not necessarily create strong accountability if a system fails or produces harmful outcomes. Hard accountability clauses such as red teaming are much rarer. Audit access, red teaming, and incident reporting each appear in fewer than 10 percent of notices. The exception to the scarcity of hard accountability rules is a relatively high incidence of data rights provisions, which spell out who owns and can use the data and models involved.
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Figure 9: Documented Governance Provisions
Share of 75-notice review frame
When agencies do include governance language, benchmarking and evaluation provisions are among the most frequently documented forms of governance language, appearing in 28 notices (37 percent). These provisions specify performance tests, evaluation metrics, or acceptance criteria. Explicit monitoring provisions, requiring repeated tracking or review of AI performance during development or operation, were confirmed in four notices (5 percent). With respect to co-occurrence, benchmarking and data rights provisions are the most frequently documented combination in the reviewed sample, appearing together in 23 of 75 notices (31 percent). These provisions address the evaluation of performance and the allocation of rights in specified data, software, or other deliverables. Benchmarking and model portability provisions appear together in 17 notices (23 percent), while benchmarking and monitoring were confirmed together in three (4 percent). Incident reporting requirements are the rarest, appearing in only three notices. In short, governance language in federal AI acquisition is inconsistent: A few agencies write comprehensive requirements, many include only basic performance measures, and over half include nothing at all.
Overall, there has been a significant increase in the U.S. government’s AI acquisition, mostly driven by defense-related contracts. U.S. AI strategy relies on the country’s continued AI leadership; therefore, the government should continue spend on AI at the federal level. As federal AI spending grows, agencies should use procurement to translate that investment into quantifiable public value creation, complete with clear expectations for performance and accountability.
Yasir Atalan is deputy director and data fellow in the Futures Lab at the Center for Strategic and International Studies in Washington, D.C. Erik Tiersten-Nyman is a research associate in the Futures Lab at CSIS. Benjamin Jensen is director of the Futures Lab and a senior fellow in the Defense and Security Department at CSIS.
