On October 31, 2019, the Department of Commerce signed an award with Accenture Federal Services to "improve patent search with artificial intelligence." USAspending.gov, the federal government's public ledger of what it spends, records the award at $44,076,744.60. Its period of performance ended on April 30, 2023. The same ledger has a column for what was actually paid on the award. That column reads 0.00.
Not blank. Zero, to the cent, on a contract that finished more than three years ago.
One row is an anecdote. So I pulled every federal contract award whose description contains the words "artificial intelligence" and that was signed or active between October 1, 2024, and September 11, 2026. There are 491 of them, across 29 awarding agencies, carrying $1,449,720,817.76 in obligations. The Department of Defense holds $937.6 million of that, just under two thirds. Then I added up the paid column, the one the Accenture row leaves at zero. Across all 491 awards it comes to $249,293,997.21.
That is 17.2 percent. For the other 83 percent of the money, the public record of federal AI spending cannot say what has been paid.
The obvious answer is that obligations are not payments. An obligation is a promise: the government has committed the money to a contract. An outlay is the check. A contract signed last spring has had no time to bill, so its outlay is zero and there is nothing wrong with that. If the population were mostly new awards, a low paid fraction would be a description of the calendar, not of the ledger.
It is partly the calendar. Of the 491 awards, 184 started in 2025 and 99 in 2026. Nobody should expect those to have paid out. So the honest test is not the total. It is the shape of the column, award by award, and in particular what it says about the awards that have had years to bill.
Here is the shape. USAspending's award-level outlay field answers the question "what was paid" in five different ways on this population.
| what the outlay column says | awards | obligations | share of dollars |
|---|---|---|---|
| the field is absent entirely | 269 | $488,560,429 | 33.7% |
| exactly zero | 98 | $409,146,653 | 28.2% |
| a partial figure | 77 | $389,522,445 | 26.9% |
| equal to the obligation, to the cent | 45 | $160,754,616 | 11.1% |
| more than the obligation | 2 | $1,736,674 | 0.1% |
The first two rows are the finding. A third of the dollars sit on awards where the ledger has no outlay field at all, not a zero, nothing. Another 28 percent sit on awards where the field exists and reads exactly zero. Only the bottom three rows, 38 percent of the money, carry a number that could be read as a payment, and the last of those rows is a number that cannot be right.
Take only the awards that began before 2024, which have had at least twenty months inside this window to bill. Twenty-four of them report exactly zero outlays, with $100.9 million obligated between them. The Accenture patent-search award is the largest. Another Accenture award for the same agency, $13.3 million, ran from May 2023 to June 2024 and reports zero. A DARPA award to Kitware, $4.8 million, ran from November 2021 to the end of 2024 and reports zero. Two more DARPA awards from the same week, to SRI International and to RTX's BBN unit, together $8.8 million, report zero. A MITRE award for Commerce, $4.2 million, ran from September 2021 to November 2024 and reports zero.
A further 24 pre-2024 awards, $47.1 million, have no outlay field at all.
These are not contracts that have not had time. Several of them are over. The work was, by the government's own dates, delivered and closed, and the column that is supposed to record the money going out records either nothing or none.
The largest single award in the population points the same way from a different angle. ECS Federal's Army contract to build prototype AI and machine-learning algorithms, signed in April 2020 and running to March 2027, carries $120,575,059.35 in obligations and $4,671,161.19 in outlays. That is 3.9 percent, six years into a seven-year contract with 47 subawards under it. Either almost nothing has been paid on the Army's biggest named AI contract, or the column does not know.
USAspending is candid about the mechanism, and it puts a date on it. Its "About the Data" guide says: "Beginning in Fiscal Year 2022, all agencies are required to submit their financial data on a monthly basis, including outlay data for award spending. Any outlay data before this period were optional for agencies to report, and thus may be incomplete."
Read that against the table. Optional reporting before fiscal 2022 explains an empty field on a 2019 award. It does not explain a zero on an award whose entire performance ran after the requirement took effect, and it does not explain the 2025 award to ECS Federal, $72.8 million for AI research and deployment, which sits in the mandatory era with an outlay of 0.00. Nor does the calendar. The requirement has been in force for four fiscal years. The awards with the largest gaps between promise and recorded payment are the ones that spent the most time inside it.
The site's methodology page describes how this happens. Agencies report award-level obligations and outlays in a file the system calls File C, and that file is linked to the contract records that give an award its description and its recipient. When the two do not link, the award still appears in searches, with its obligation, and the outlay side is simply not there. The Government Accountability Office reviewed federal spending-transparency data quality in 2022 and found the agency plans for it had gaps, outlay tracking among them. None of this is hidden. It is documented, dated, and then routinely ignored by everyone who quotes the obligation figure as if it were the spend.
The cheapest demonstration that the outlay column is not a clean measurement is the last row of the table. Aperio Global's award 70SBUR24F00000223 shows $1,538,850.00 obligated and $1,539,034.52 paid. HCC Consulting's award 72MC1025C00003 shows $197,824.49 obligated and $198,319.43 paid. Between them, the ledger reports $679.46 in payments on money that was never committed.
Small amounts. Possibly a modification recorded on one side and not the other. But a column that can exceed its own ceiling is a column being filled by hand, from different systems, on different schedules, and that is the same column the 17.2 percent comes from. The 45 awards where outlays equal obligations to the cent deserve the same suspicion in the other direction. Tuknik Government Services' Interior award for AI support to the Pentagon's Joint Artificial Intelligence Center shows $106,725,460.68 obligated and $106,725,460.68 paid. That can be true. It is also exactly what a field looks like when someone copied the obligation into it.
A piece about a ledger that cannot add up should show its own sums being checked. The first pass at these numbers used the 501 rows the search endpoint returned. Only 491 of them were distinct awards. Ten were repeats of rows already returned on an earlier page, a Stevens Institute award among them, and the uncorrected obligation total was $1,496,023,651.74, which is $46.3 million too high. Every figure here is computed with the duplicates dropped on the award's own identifier, and the count of dropped rows is reported rather than quietly applied, so anyone re-running the query can tell whether the endpoint's behaviour has changed rather than inheriting a silent fix.
The correction also moved a number in the research file this essay was written from. That file counted 26 pre-2024 awards with zero outlays and $111.8 million between them; on the deduplicated table the count is 24 and the sum $100.9 million. The essay carries the deduplicated figures, and the difference is two rows that the API served twice.
Every procurement announcement, agency press release and chart of "federal AI spending" uses obligations, because obligations are the number that exists for every award. The column that answers the question a taxpayer actually asks, what has been paid, is populated well enough on this population to answer for 17.2 percent of the money, and for nearly a third of it the field does not exist.
That matters more for artificial intelligence than for most categories, because these are the contracts cited as evidence of a federal AI build-out. A $1.45 billion figure that is 62 percent unaccounted for in the payment column is a measurement of intent. It is being read as a measurement of activity. The distinction can be recovered, but only by reading the column that is mostly empty, and by saying which awards it is empty on.
None of this says the money was wasted or the work not done. An absent outlay is a reporting fact. The Accenture patent-search contract may have delivered exactly what it promised; the ledger has no way to tell you, and neither do I. What the record supports is narrower and, I think, more useful: when someone tells you how much the federal government is spending on artificial intelligence, ask which column they read.
The federal award figures in this piece were computed from the USAspending API v2 on 12 September 2026 and rechecked from the saved response before writing: 491 distinct contract awards, $1,449,720,817.76 obligated, $249,293,997.21 in the award-level outlay column. The query is the one in Sources below, so anyone can re-run it and compare. Two controls ran beside it: a positive one, in which the ECS Federal award must appear at its known obligation, and a negative one, in which a nonsense description filter must return nothing. Ten duplicate rows were dropped and counted. The population is awards whose description contains "artificial intelligence"; it will include boilerplate mentions and exclude AI work described as machine learning or autonomy, and the claim is about what the ledger says of the awards that call themselves AI, not about all of them.
Part of Where the Number Came From, on how a published number is a fact about the way it was measured: Stanford says 12% to 66%, but 12% of what? · The safety score is a fact about the test rig · Tracing the 2026 AI-failure statistics to a primary
Sources:
POST /api/v2/search/spending_by_award/, filters: award types A, B, C, D; time period 2024-10-01 to 2026-09-11; description "artificial intelligence". Run 12 September 2026. https://api.usaspending.gov/api/v2/search/spending_by_award/GET /api/v2/awards/CONT_AWD_W911QX20C0023_9700_-NONE-_-NONE-/ (the ECS Federal award detail, including the subaward count and period of performance).