The FBI's AI use is up 605% — and there is no baseline for it
Kash Patel put a number on the bureau's AI adoption that nobody outside it can check. Elsewhere, a 294,000-parameter voice model shows what "small" is supposed to mean.
FBI Director Kash Patel says he has increased the bureau's use of AI by 605% since taking the seat — a figure offered on Fox News with no baseline, no metric and no audited inventory behind it. He paired the number with specific outcomes: AI triage on a tip that "stopped a school massacre in North Carolina" and about half a dozen other states, and the assertion that "every major tech company in the world" is now embedded inside the FBI, rebuilding its internet capabilities, its classified systems and the AI in its counterterrorism program. The direction of travel is corroborated even where the percentage isn't. The bureau's chief AI officer, Katie Noyes, has said the FBI runs more than 100 approved AI use cases — double the 50 it listed in its January inventory — each cleared through an internal AI review board, and Patel told a Senate hearing this month that he can reach the major AI CEOs directly. Cost, not appetite, is the binding constraint: Noyes has called the price point "a little steep," which is why the bureau buys tokens through cloud providers and prioritizes model-agnostic tooling it can swap out.
That is the part worth holding onto. A 605% claim is the kind that becomes a fact through repetition, and the bureau's disclosure record already lags its deployment record — an inventory that trails reality is precisely how oversight fails, because nobody outside can tell a real capability jump from a talking point. The second thing: a system that triages tips, generates call transcriptions and correlates contacts is a surveillance surface as much as a force multiplier. Patel confirmed under oath in March that the FBI buys location data on Americans, which is a harder fact than any adoption percentage.
A team called Ampixa has open-sourced sanoTTS, a text-to-speech family whose smallest voice is 294,000 parameters — a complete int8 stack in 337 KB that runs on a $3 ESP32-S3 chip or entirely client-side in a browser via WebAssembly. The largest voice, "heart," is 2.27 million parameters at 24 kHz; the project reports the best SCOREQ and UTMOS scores in the sub-15M class and frames the comparison bluntly — Kokoro is roughly 279 times larger than its smallest voice and 36 times larger than its biggest. Its own ablation says quality lives in the decoder: doubling it from 1.09 million to 1.84 million parameters moves SCOREQ from 3.70 to 4.16, so the tiny voices trade a little fidelity for an enormous deployment surface. Weights are GPL-3.0, with a Python package, an npm web build, Arduino and ESPHome targets, and a live demo where all nine voices synthesize in the browser.
The interesting number here isn't the parameter count, it's the target. Speech is the input mode that makes small local agents usable, and a voice that fits in 337 KB changes what hardware the assistant needs to live on — no GPU, no API call, no network. The catch is voice quality and language coverage: the published evaluations lean on short held-out sentence sets, and the project ships a distillation recipe rather than a claim of parity with large commercial voices.
What to watch: whether the FBI's next AI inventory reflects anything like the growth Patel is advertising, and whether sanoTTS's sub-15M quality lead survives longer, harder evaluation sets.
Should agencies be required to publish an AI use-case count before their directors can quote a growth figure? Tell us in the comments.
Sources: Mediaite — Kash Patel boasts he's increased the FBI's AI use by 605% · Tom's Hardware — Patel claims 605% AI increase at the FBI · FedScoop — FBI is using much more AI than what it previously disclosed · The Guardian — Patel admits under oath the FBI is buying location data on Americans · sanoTTS (GitHub) · sanoTTS live demo · r/MachineLearning — Inside sanoTTS
