For immediate release
$27.7K in federal contracts naming AI performed in Tennessee this fiscal year, index finds
Tennessee ranks 32 of 51 states by federal AI-labelled contract obligations, 0.0 % of the U.S. total, $0.00 per resident.
NASHVILLE, TENN., Sept. 9, 2026 — Federal prime contracts whose descriptions name artificial intelligence, machine learning or language models, performed in Tennessee, total $27.7K for fiscal year 2026 to date, according to the AI Burn Clock's daily state release drawn from USAspending.gov. That places Tennessee 32 of 51 by place of performance, 0.0 % of the U.S. total, or $0.00 per resident. The state's debt at the end of fiscal year 2023 was $8.0B (Census Bureau).
The figure is a floor: AI work inside larger contracts is usually not labelled, and grants, internal spending and cloud consumption are excluded. Applied to it, the index's reference factors put avoidable reading, the share of that spending's inference that an agent would not have needed with a local index, at $2K a year, a scenario computed from a published floor rather than an account of the state's spending.
The remedy is open source and runs inside a state's own environment: XERJ (github.com/xerj-org/xerj, Apache-2.0) indexes a folder in one command so that an agent retrieves the passage it needs instead of reading whole files. A pilot fits on one laptop and needs no procurement. State offices can request a one-page memo with these figures and their sources at [email protected].
Figures in this release
| Series | Value | Source |
|---|---|---|
| Federal contracts naming AI, FY2026 to date, performed in Tennessee | $27.7K | USAspending.gov |
| Rank among 51 | 32 | AI Burn Clock, Table 3 |
| Per resident | $0.00 per resident | Census Bureau, Vintage 2024 |
| State debt, end of FY2023 | $8.0B | Census Bureau, ASFIN |
| Avoidable reading, scenario | $2K / year | AI Burn Clock, reference factors |
About the AI Burn Clock
The AI Burn Clock (aiburnclock.org) is an independent statistical index of the cost of retrieval by reading in AI systems. It reproduces official series from the U.S. Treasury, USAspending.gov and the Census Bureau as published, and estimates the avoidable cost of AI agents reading whole files with a four-factor method whose every parameter is a control on the page. It is revised daily at 00:00 UTC and maintained by the XERJ community. It is not affiliated with any government agency. Method: aiburnclock.org/methodology. Data: aiburnclock.org/data.json.
About XERJ
XERJ is an open-source local search engine for AI agents: one Rust binary, Elasticsearch-compatible on port 9200, that indexes any folder so an agent retrieves the passage it needs instead of reading whole files. It is published under the Apache-2.0 license at github.com/xerj-org/xerj, with documentation at xerj.org. A Claude Code plugin that adds XERJ as local memory and produces a per-session score card is at github.com/nikolaichuk7/xerj-plugins.
Media contact
[email protected] · Serhii Nikolaichuk, maintainer · Interviews, data pulls and state or agency memos on request. Releases are issued for every state and may be republished by any outlet with attribution.
