Profound Raised $96M to Show You Your AI Score. Here's What to Do When It's Low.
Profound just hit unicorn status tracking AI brand citations. Most brands running the tool for the first time are finding gaps. Here's the earned media playbook for closing them.
Profound raised $96 million at a $1 billion valuation, backed by Lightspeed, Sequoia, and Kleiner Perkins. The AI visibility tracking platform now works with 700+ enterprise companies — including 10% of the Fortune 500 — to show marketers how frequently AI engines cite and recommend their brand. The technology shows the problem. It does not solve it. Here is the earned media playbook for closing the gap.
Key Takeaways
- AI visibility scores reflect earned media footprint. AuthorityTech's analysis across 200+ client programs shows 82–89% of AI-generated answers cite third-party earned media — Forbes, TechCrunch, WSJ, Bloomberg — over brand-owned content.
- Measurement without a mechanism is a dashboard, not a strategy. Profound, BrightEdge AI, and similar tools show citation frequency but have no mechanism for earning the placements that drive it.
- One tier-1 placement outweighs dozens of lower-authority mentions. Publications with the highest editorial standards and longest digital history dominate AI training data and retrieval systems.
- The earned media playbook has three moves: audit your current footprint, prioritize tier-1 publications AI engines actually cite, and combine earned placements with citation-optimized owned content.
- The action window is narrowing. Gartner projects traditional search to decline 25–50% by 2028, while AI search traffic grows at 9.7x year over year. Early movers in AI citation authority create compounding advantages.
What an AI visibility score actually measures
An AI visibility score measures citation frequency — how often ChatGPT, Gemini, Perplexity, and Claude mention your brand when answering category queries. Profound CEO James Cadwallader told Fortune: "In the future, every company on the planet will care deeply about how AI talks about and surfaces their brand." What the platform shows is your current citation frequency. What it does not show is what drives those citations at the source.
The source is not schema markup or FAQ sections. Those are table stakes. AuthorityTech's analysis across 200+ client programs shows that 82–89% of AI-generated answers cite third-party earned media over brand-owned content. Your AI visibility score is primarily a reflection of your earned media footprint in publications that AI systems weight as authoritative.
Why earned media drives AI citation scores
AI engines weight publications with editorial independence, long crawl histories, and cross-platform authority when assembling answers — the same publications that built brand credibility for decades. Fortune's reporting notes that up to 90% of cited sources in AI answers change over time, and different models draw from distinct source sets. Score volatility usually means shallow, inconsistent coverage rather than a deep, persistent earned media footprint.
Search Engine Land's GEO guide confirms that the publications dominating AI citation share in every category are those with the strongest editorial authority and the longest history of AI training data inclusion. GEO — a distribution tactic within Layer 4 of the Machine Relations framework — works by making content extractable. But the upstream signal is earned media: the placements that create the authority GEO distributes.
The three-move playbook for closing an AI citation gap
Move 1: Audit your current earned media footprint
Before touching your website, audit where your brand has been covered in the last 18 months. Not press releases. Not guest posts on your own blog. Third-party editorial coverage: news articles, profile pieces, product reviews, and analysis in authoritative publications. If your coverage is thin — a handful of regional outlets, a few trade publications, or mostly self-published content — that is the gap.
Move 2: Prioritize publications AI engines actually cite
Not every publication is equal for AI citation purposes. The publications that appear most frequently in AI training data and retrieval are those with the highest editorial standards: Forbes, TechCrunch, WSJ, Fast Company, Bloomberg, Inc., Wired, and their authoritative vertical equivalents. One Forbes feature moves the citation needle more than 20 medium-tier trade press mentions. One TechCrunch piece with a named executive quote creates an entity signal that persists across AI training runs and real-time retrieval.
Move 3: Combine earned placements with citation-optimized owned content
Every earned media placement needs somewhere to point — an owned content asset that reinforces the entity signal the placement creates. That means a GEO-optimized blog post defining your brand's core claims with specific data, structured Key Takeaways AI engines can extract, and FAQ sections answering the exact questions users ask in your category. This is Machine Relations in practice: earned authority plus citation architecture plus entity optimization working together.
AI visibility: measurement tools versus earned media mechanism
| Layer | What it does | Example | Limitation |
|---|---|---|---|
| Measurement | Shows citation frequency across AI engines | Profound, BrightEdge AI | Shows the gap but cannot close it |
| Content optimization (GEO) | Makes owned pages extractable by AI engines | Structured H2s, FAQ, schema | Only works if upstream authority exists |
| Earned media (Machine Relations) | Creates the authority signal AI engines cite | Tier-1 placements in Forbes, TechCrunch, WSJ | Requires editorial relationships and time |
What this looks like in practice
A Series B startup opens its AI visibility dashboard and sees a citation frequency of 3% for its primary category keywords. Competitors are at 15–22%. The brands ranking in AI answers have 12–24 months of consistent tier-1 earned media coverage.
The fix: launch a systematic earned media program targeting 2–4 tier-1 placements per month, combined with refreshed blog architecture using citation-optimized structure on every key page. AuthorityTech's data shows brands running this at scale achieve AI visibility gains 200x faster than those relying on occasional content. Conductor's 2026 research confirms the urgency: 32% of digital marketing leaders named GEO their top priority, up from 12% in 2025.
Measurement versus mechanism
AI visibility tools show the gap. Earned media closes it. The question is not "how do I optimize my website?" The first question is "where has my brand appeared, in credible third-party editorial coverage, in publications that AI engines trust?" The gap in that answer is the gap in the score.
Start with an AI visibility audit to see where your brand's citation authority stands and where the highest-leverage gaps are. Gartner projects traditional search to decline 25–50% by 2028 while AI search traffic grows at 9.7x year over year. The companies that move fastest on the action layer will own their category's AI citation share before competitors figure out what the dashboard is telling them.
FAQ
What does a low AI visibility score actually mean?
A low score means AI engines like ChatGPT, Gemini, and Perplexity are not citing your brand when answering questions in your category. The primary driver is usually a thin earned media footprint — insufficient third-party coverage in authoritative publications that AI systems weight as trusted sources. AuthorityTech's analysis shows 82–89% of AI citations come from third-party earned media, not brand-owned content.
How quickly can earned media improve an AI visibility score?
Brands running systematic earned media programs — 2–4 tier-1 placements per month combined with citation-optimized owned content — typically see measurable improvements within 60–90 days. The compounding effect accelerates: 34% of AI citations in any category go to a single dominant source, so brands that build early earned authority create moats that become harder to close.
Which publications matter most for AI citation scores?
Forbes, TechCrunch, WSJ, Bloomberg, Fast Company, Wired, Inc., and authoritative vertical publications in your specific category. These are the publications that appear most frequently in AI training data and real-time retrieval systems. One tier-1 placement produces a more durable AI citation signal than dozens of lower-authority trade press mentions.
What is the difference between AI visibility measurement and Machine Relations?
AI visibility tools like Profound and BrightEdge AI are the measurement layer — they show citation frequency. Machine Relations, coined by Jaxon Parrott at AuthorityTech, is the full discipline for building AI citation authority: earned media placements that AI engines cite as trusted sources, citation-optimized owned content, and structured entity signals working together. Measurement shows the score. Machine Relations changes it.