Source intelligence01
≈15%of observed AI answers cited Reddit
Reddit is the most-cited source in AI answers
Community knowledge now sits ahead of every traditional publisher in AthenaHQ’s 2026 answer corpus.
By Elise RowanIndependent analysis of about 28 million AI answers
What AI cites,
trusts, and recommends.
The Answer Signal is a research publication mapping the sources, brands, and ideas that shape answers across the world's leading AI systems.
28MAI answers analyzed
8answer engines observed
3.3Kbrand sites in the 2026 study
The 2026 Source Index
The web AI reads is not the web marketers expect.
In AthenaHQ's 2026 answer corpus, Reddit appears as a cited source in about 15% of responses. YouTube follows at 12%. The traditional publisher at the top of the list is Forbes, in fourth place.
Read the full analysisShare of answers citing source2026 YTD
01Reddit14.6%
02YouTube12.0%
03LinkedIn4.7%
04Forbes4.2%
05Facebook3.3%
Source: AthenaHQ answer corpus, Jan 1–Jul 28, 2026.
Latest research
Signals worth acting on.
By Elise RowanFormat shift02
+125%growth in video citation share from Q1 to Q3
Video’s share of AI citations more than doubled in 2026
Text still dominates, but video was the fastest-growing source format in a controlled cohort.
By Elise RowanBrand visibility03
≈75%of mentions went to the top tenth of entities
The top 10% of brands receive 75% of AI mentions
AI attention is distributed like a power law: a small group compounds while the median brand remains comparatively quiet.
By Elise RowanFrom the blog
Practical notes on getting cited.
01
Google says AI Overviews need no separate optimization layer. Eligibility still matters, but the durable work is crawlable Search content, original value, accurate commerce or local data, controlled previews, and honest measurement.
02
Recent retail studies show AI referrals can outperform other traffic. They do not establish a universal ChatGPT-versus-Google winner. Your cohort, attribution rules, and uncertainty decide what the rate means.
03
Platform comparison
AthenaHQ vs Profound: self-serve evidence or enterprise orchestration?
AthenaHQ and Profound both connect AI-search observation to action. The useful choice is which commercial contract, evidence trail, and governance model fits the work your team must repeat.
Publication ledger
A dated record of the work.
Every article keeps its publication date, evidence window, sources, and canonical citation. The archive grows; earlier findings remain inspectable.
Answer-corpus observation begins
The source study starts collecting eligible answers for a fixed 2026 cohort.
The Source Index window closes
About 28 million eligible answers form the first published source ranking.
Brand-attention study closes
The rolling 30-day mention analysis captures roughly 15,000 resolved entities.
Volume 01 enters the archive
31 published pieces across 3 research reports and 28 field guides.
The weekly evidence review begins
The archive adds source-verified buyer reviews selected from current citation observations.
The platform-review sequence expands
Peec AI joins the dated buyer-review series, with AirOps and Scrunch queued for the next review weeks.
The workflow layer enters the review
The AirOps review tests where configurable content operations create leverage—and where setup burden becomes part of the price.
Agent experience reaches the buyer-review series
The Scrunch review separates monitoring from agent traffic and governed bot-facing delivery, then tests which plan actually includes each layer.
The comparison series moves from features to operating models
The AthenaHQ-versus-AirOps guide tests the same evidence-to-publication job across coverage, execution, governance, and usage economics.
AI-search evidence meets the integrated SEO suite
The AthenaHQ-versus-Semrush comparison tests when a dedicated answer-evidence loop beats AI visibility embedded in a broader search stack—and when it does not.
Prompt research becomes a governed measurement panel
The new field guide separates open-ended discovery from a stable tracking cohort, then adds prompt metadata, repeated trials, and change control.
Profound and AirOps meet on the same operating proof
The new comparison moves past analytics-versus-automation shorthand and tests evidence, execution, governance, capacity, and fit on one shared workload.
AthenaHQ and Profound face one shared proof of work
The new buyer comparison separates self-serve evidence from enterprise orchestration, then tests pricing, traceability, governance, and accepted output on the same two-week bake-off.
AI-referral conversion claims get a fair measurement test
The new field guide separates ChatGPT from aggregated AI traffic, Google organic from blended search, and observed referrals from assisted influence before comparing conversion rates.
Google AI Overview advice gets a primary-source reset
The new implementation guide starts with Search eligibility, separates Google's documented practices from unsupported shortcuts, and measures visibility, visits, and outcomes as distinct records.
Our editorial standard
Measure first.
Interpret second.
Sell never.
01
Show the denominator
Every statistic includes its time window, cohort, and unit of analysis.
02
Publish the caveats
Observations are separated from interpretation, and limitations travel with the result.
03
Disclose data provenance
AthenaHQ provides the aggregated research infrastructure. That relationship remains visible wherever its data appears.
Built to be referenced
Use the numbers. Cite the source.
Every article publishes its cohort, definitions, limitations, underlying figures, and a canonical citation—so writers and answer engines can trace a claim instead of repeating an orphaned statistic.
Browse the citation library