SEC 10-K AI disclosure intelligence (annual)

Commercial use OKFresh

The full snapshot isn't published yet — check back after the next refresh.Download sample CSV Download sample JSONSample rows only (up to 20) — not the complete dataset.

Yearly panel of AI-phrase mentions in SEC 10-K filings, 2016-2026: for four AI phrases (artificial intelligence, machine learning, generative AI, large language model) the number of 10-K filings mentioning the phrase per filed year, the share of all 10-Ks, year-over-year momentum, and the industry diffusion across SIC major groups (from the API's own SIC aggregation, top-30 codes). Derived from the keyless SEC EDGAR full-text search API; 2026 is a partial year.

Source: SEC 10-K AI disclosure intelligence (derived)
Rows: 471
Columns: 14
Source cadence: Yearly
Last refreshed: Sep 25, 2026
Theme: markets

Column	Type	Description
filed_year	integer	Calendar year the 10-K was filed with the SEC (2016-2026). 2026 is a partial year (filings through the as_of date). (unit: year)
phrase	string	Closed taxonomy of exact-phrase queries: artificial_intelligence, machine_learning, generative_ai, large_language_model. Phrases overlap in the wild — rows are per phrase, never summed.
sic_major_group	string	SIC 2-digit major group of the filer's industry, or 'ALL' for the all-industry rollup. The breakdown covers only the API's top-30 SIC codes per (year, phrase); smaller industries are omitted, never imputed.
sic_major_group_name	string	Standard SIC manual name for sic_major_group.
n_10k_total	integer	All 10-K filings (forms 10-K and 10-K/A) filed that year — the denominator behind mention_share_pct. (unit: count)
n_filings_mentioning	integer	10-K filings whose full text contains the exact phrase (for ALL rows); filings in this major group for group rows. (unit: count)
mention_share_pct	float	100 * n_filings_mentioning / n_10k_total: share of all 10-K filings mentioning the phrase. (unit: percent)
ai_concentration_pct	float	Group rows: the group's share of all phrase-mentioning filings that year (100.0 on ALL rows). Null when the year-phrase total is zero. (unit: percent)
yoy_growth_pct	float	Year-over-year percent change of n_filings_mentioning within the (phrase, group); null for 2016 and when the prior year is absent or zero. (unit: percent)
yoy_share_pp_change	float	Year-over-year change of mention_share_pct in percentage points; null for 2016 and when the prior year is absent. (unit: percentage points)
is_partial_year	integer	1 for the latest filed year (2026, filings only through the as_of date); 0 otherwise. (unit: 0/1)
as_of	string	Snapshot date (UTC); identical input re-ingests hash-identically. (unit: ISO date)
source_url	string	Exact SEC EDGAR full-text search API URL behind the (year, phrase) cell. (unit: URL)
row_hash	string	Stable 16-hex sha256 over filed_year, phrase, sic_major_group, as_of, n_filings_mentioning and n_10k_total.

First 10 sample rows — a preview, not the complete dataset.

filed_year	phrase	sic_major_group	sic_major_group_name	n_10k_total	n_filings_mentioning	mention_share_pct	ai_concentration_pct	yoy_growth_pct	yoy_share_pp_change	as_of	source_url	row_hash
2,016	artificial_intelligence	ALL	All industries	8,388	52	0.62	100	—	—	2026-09-25	https://efts.sec.gov/LATEST/search-index?q=%22artificial+intelligence%22&forms=10-K&dateRange=custom&startdt=2016-01-01&enddt=2016-12-31	16d6b50185730e7b
2,016	artificial_intelligence	73	Business Services	8,388	23	0.27	44.23	—	—	2026-09-25	https://efts.sec.gov/LATEST/search-index?q=%22artificial+intelligence%22&forms=10-K&dateRange=custom&startdt=2016-01-01&enddt=2016-12-31	89698db4819122f9
2,016	artificial_intelligence	28	Chemicals And Allied Products	8,388	4	0.05	7.69	—	—	2026-09-25	https://efts.sec.gov/LATEST/search-index?q=%22artificial+intelligence%22&forms=10-K&dateRange=custom&startdt=2016-01-01&enddt=2016-12-31	d30e077f893888f8
2,016	artificial_intelligence	67	Holding And Other Investment Offices	8,388	4	0.05	7.69	—	—	2026-09-25	https://efts.sec.gov/LATEST/search-index?q=%22artificial+intelligence%22&forms=10-K&dateRange=custom&startdt=2016-01-01&enddt=2016-12-31	9b39bf6707883e9c
2,016	artificial_intelligence	35	Industrial And Commercial Machinery And Computer Equipment	8,388	3	0.04	5.77	—	—	2026-09-25	https://efts.sec.gov/LATEST/search-index?q=%22artificial+intelligence%22&forms=10-K&dateRange=custom&startdt=2016-01-01&enddt=2016-12-31	92acf436d3641b8e
2,016	artificial_intelligence	36	Electronic And Other Electrical Equipment And Components	8,388	3	0.04	5.77	—	—	2026-09-25	https://efts.sec.gov/LATEST/search-index?q=%22artificial+intelligence%22&forms=10-K&dateRange=custom&startdt=2016-01-01&enddt=2016-12-31	de06cf54f8c135d8
2,016	artificial_intelligence	38	Measuring, Analyzing, And Controlling Instruments	8,388	3	0.04	5.77	—	—	2026-09-25	https://efts.sec.gov/LATEST/search-index?q=%22artificial+intelligence%22&forms=10-K&dateRange=custom&startdt=2016-01-01&enddt=2016-12-31	42dfad1835245011
2,016	artificial_intelligence	62	Security And Commodity Brokers, Dealers, Exchanges, And Services	8,388	2	0.02	3.85	—	—	2026-09-25	https://efts.sec.gov/LATEST/search-index?q=%22artificial+intelligence%22&forms=10-K&dateRange=custom&startdt=2016-01-01&enddt=2016-12-31	e99b3b31b7b4a41d
2,016	artificial_intelligence	78	Motion Pictures	8,388	2	0.02	3.85	—	—	2026-09-25	https://efts.sec.gov/LATEST/search-index?q=%22artificial+intelligence%22&forms=10-K&dateRange=custom&startdt=2016-01-01&enddt=2016-12-31	d7626715c1467818
2,016	artificial_intelligence	80	Health Services	8,388	2	0.02	3.85	—	—	2026-09-25	https://efts.sec.gov/LATEST/search-index?q=%22artificial+intelligence%22&forms=10-K&dateRange=custom&startdt=2016-01-01&enddt=2016-12-31	6d6f98648809a626

Profiled Sep 25, 2026 from snapshot 20260925T153536Z-d87379785aff

Measured

Completeness: 96.8%
Rows: 471
Columns: 14
Columns with gaps: 2

Column	Missing	Distinct	Range	Distribution
filed_yearbigint	0%	12	2,016 → 2,026median 2,023
phrasevarchar	0%	3	—	artificial_intelligence177 machine_learning170 generative_ai73
sic_major_groupvarchar	0%	39	—	ALL44 7332 2829
sic_major_group_namevarchar	0%	42	—	All industries44 Business Services32 Chemicals And Allied Products29
n_10k_totalbigint	0%	9	6,533 → 8,401median 7,643
n_filings_mentioningbigint	0%	145	0 → 3,838median 12	5 outside 1st–99th percentile
mention_share_pctdouble	0%	157	0 → 58.75median 0.17	5 outside 1st–99th percentile
ai_concentration_pctdouble	0%	278	0.54 → 100median 2.27	5 outside 1st–99th percentile
yoy_growth_pctdouble	24%	194	-66.7 → 4,000median 38.85	8 outside 1st–99th percentile
yoy_share_pp_changedouble	21.4%	107	-0.78 → 16.33median 0.07	8 outside 1st–99th percentile
is_partial_yearbigint	0%	2	0 → 1median 0
as_ofvarchar	0%	1	—	2026-09-25471
source_urlvarchar	0%	44	—	https://efts.sec.gov/LATEST/search-index?q=%22artificial+intelligence%22&forms=10-K&dateRange=custom&startdt=2024-01-01&18 https://efts.sec.gov/LATEST/search-index?q=%22artificial+intelligence%22&forms=10-K&dateRange=custom&startdt=2025-01-01&18 https://efts.sec.gov/LATEST/search-index?q=%22machine+learning%22&forms=10-K&dateRange=custom&startdt=2025-01-01&enddt=218
row_hashvarchar	0%	447	—	0024a117de0e544d1 002d6ce1619a5b651 00ef92ee2b4489ad1

Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/sec_ai_disclosure/sec_10k_ai_disclosure_annual" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/sec_ai_disclosure/sec_10k_ai_disclosure_annual").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

API endpoint: https://datazimuts.com/v1/datasets/sec_ai_disclosure/sec_10k_ai_disclosure_annual

Tip: fetch /llms.txt for the full machine-readable catalog.

Cite this snapshot

Pinned to snapshot 20260925T153536Z-d87379785aff and its content hash, so readers get exactly the data you used.

APA

SEC 10-K AI disclosure intelligence (derived). (2026). SEC 10-K AI disclosure intelligence (annual) [Data set, snapshot 20260925T153536Z-d87379785aff, sha256 d87379785aff]. Datazimuts. Retrieved 2026-09-25, from https://datazimuts.com/en/datasets/sec_ai_disclosure/sec_10k_ai_disclosure_annual?snapshot=20260925T153536Z-d87379785aff

BibTeX

@misc{dz_sec_ai_disclosure_sec_10k_ai_disclosure__d8737978,
  title        = {{SEC 10-K AI disclosure intelligence (annual)}},
  author       = {{SEC 10-K AI disclosure intelligence (derived)}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/en/datasets/sec_ai_disclosure/sec_10k_ai_disclosure_annual?snapshot=20260925T153536Z-d87379785aff}},
  note         = {Snapshot 20260925T153536Z-d87379785aff, sha256 d87379785affdbc621d1d135460b1da6952abd7bc0d6e0945e8c2329c7a62982; accessed 2026-09-25}
}

Embed a table or a chart

Paste this into any page. The embed is pinned to the same snapshot, follows the reader's light or dark setting, and always shows the source, license and a link back.

Type

X axis

Value

Aggregate

Theme

HTML

<iframe src="https://datazimuts.com/embed/chart?dataset=sec_ai_disclosure%2Fsec_10k_ai_disclosure_annual&lang=en&theme=auto&snapshot=20260925T153536Z-d87379785aff&x=filed_year&y=filed_year&agg=avg" title="SEC 10-K AI disclosure intelligence (annual)" width="100%" height="380" style="border:0" loading="lazy"></iframe>

SEC 10-K AI disclosure intelligence (annual)

Follow this dataset

Cite this snapshot

Embed a table or a chart

Use your own AI key