AI Share of Voice Definition and Formula by Question Shape

AI Share of Voice is the percentage of brand mentions captured across a defined set of buyer prompts. Its formula is (your brand mentions ÷ total brand mentions for all tracked brands) × 100. It measures breadth, not citation depth or recommendation quality: Share of Citation and the composite AI Visibility Score answer different questions. To measure the full visibility picture, test 25–50 buyer prompts across ChatGPT, Perplexity, Gemini, and Google AI Mode.

One blended number is the wrong unit. In the Machine Relations Index, the neutral public instrument that measures which sources AI engines actually cite, the AI Visibility & GEO category splits into six buyer question shapes — and in the 2026-10-13 release no domain is in the top three of all six. YouTube and Reddit each reach four; every other leader changes with the question, and the top three moved on five of the six shapes in the 25 days since the 2026-09-18 release. A share-of-voice score pooled across prompt shapes measures the composition of your prompt library, not your standing in the market. Measure each shape on its own denominator first, then roll up.

This page is the definition and the formula. Two companion pages carry the operational detail and are written to be read after it, not instead of it: the AI share of voice measurement contract fixes the prompt set, providers, aliases and denominator before you collect anything, and the AI share of voice reporting protocol covers sampling, auditable counting rules and the per-cycle report. For how the number reads against brand visibility and authority, and which metric to read beside it, see the glossary entry on AI share of voice. Share of voice is one metric inside a wider stack; for the other six and how they fit on one dashboard, read The Seven Metrics That Measure AI Search Visibility.

Why One Blended Number Is the Wrong Unit

Machine Relations publishes an observed index of which domains AI engines cite, segment by segment, refreshed daily. The numbers below are read from release mri_score_v2.1+2026-10-13+6fc79cb02bbf, window 2026-05-10 to 2026-10-13, for the AI Visibility & GEO category. Each shape keeps its own run denominator; the index does not pool rates across shapes, and neither should your report.

Buyer question shape	Observed runs	Cited domains	#1 source	#2 source	#3 source
Best tools	202	517	Seranking.com 23.76%	Reddit 20.30%	YouTube 19.31%
How buyers choose	200	619	YouTube 15.50%	Stackmatix.com 13.00%	Brandviz.ai 9.00%
Is it worth it	224	612	Reddit 20.09%	HubSpot 17.41%	Stackmatix.com 16.96%
Problem-first research	220	603	YouTube 20.91%	Reddit 19.09%	LinkedIn 17.27%
Top lists	160	706	YouTube 22.50%	Tryprofound.com 21.88%	Therankmasters.com 18.13%
Comparisons	220	518	HubSpot 26.36%	Semrush.com 17.27%	Reddit 16.36%

Four things follow directly from that table, and each one changes how the metric should be built.

No domain is stable across all six; two come close. YouTube is in the top three on four shapes (best tools, how buyers choose, problem-first research, top lists) and is fifth on is-it-worth-it and sixth on comparisons. Reddit is in the top three on four (best tools, is it worth it, problem-first research, comparisons) and is twelfth on how buyers choose and ninth on top lists. Beyond those two, the leaderboard reshuffles: Brandviz.ai is third when buyers ask how to choose and nowhere near the top three of any other shape, while Semrush.com is second only on comparisons. If your measurement blends shapes, you are averaging six different competitive sets into one score.

The denominators are not equal. The six shapes carry 160 to 224 observed runs each. Pooling them weights every shape by how often it ran in the sample, not by how often your buyers ask it. Two vendors can report "AI share of voice" from the same engines on the same day and disagree entirely because their prompt libraries lean toward different shapes.

No one owns the category. The highest citation rate any source reached on any shape is 26.36%, and each shape has between 517 and 706 distinct cited domains. In a market this fragmented, a low share-of-voice number is not proof of failure — the question is whether you appear at all on the shapes your buyers actually use.

Source class beats source count. YouTube and Reddit hold the top slot on four of the six shapes, while comparisons are led by a vendor-owned marketing site, HubSpot. Buying more owned content does not move a leaderboard shaped like that. Independent corroboration does.

What Moved in 25 Days

The same category was published in release mri_score_v2.0+2026-09-18+8fa38e54dd0a. Both windows start on 2026-05-10, so the newer one adds 25 days of runs to the same base; the rates above are cumulative and a shift is the net of those added runs.

Buyer question shape	Top three changed since 2026-09-18	#1 changed
Best tools	Seranking.com enters; Therankmasters.com leaves	Yes: YouTube to Seranking.com
How buyers choose	Stackmatix.com enters; Gen-optima.com leaves	No: YouTube
Is it worth it	HubSpot enters; YouTube drops to fifth	No: Reddit
Problem-first research	None	No: YouTube
Top lists	Tryprofound.com and Therankmasters.com enter; Seranking.com and Yotpo.com leave	No: YouTube
Comparisons	Semrush.com enters; YouTube drops to sixth	No: HubSpot

Six of the eighteen top-three slots turned over, and the top three changed on five of the six shapes. The headline of the earlier release no longer holds: YouTube was then in the top three of all six shapes, and it is now in four. In this interval the #1 slot was steadier than the top-three membership: the leader changed on one shape, while the set of three changed on five. A pooled score hides which of the two moved. Read each shape against the release it was measured in, and re-measure on a schedule rather than treating one reading as a standing.

The practical rule: report six numbers and a roll-up, never one number. Stratify the prompt library by shape, hold each shape's denominator separate, and compare yourself to the competitive set that actually appears on that shape. Then read where the source ecosystem around your brand is thin — that is the number you can move.

What AI Share of Voice Actually Measures

Traditional share of voice counted media impressions and ad placements. You could buy it. AI share of voice cannot be bought. It is the outcome of how AI systems evaluate your brand's credibility, relevance, and extractability every time a user asks a question in your category.

Machine Relations Research defines AI Share of Voice as one brand's qualifying mentions divided by every qualifying brand mention observed in the same answers, under a declared panel of prompts, engines, counting rules and time windows — a mentions-based breadth metric whose denominator is open rather than a preset competitor list. A count of answers that merely mention the brand is a visibility rate, not a share. When you weight that presence together with citations, recommendations, and source absorptions across all tracked models and divide by the total weighted visibility for all tracked brands, you are no longer measuring Share of Voice alone: you are calculating the composite AI Visibility Score, the roll-up that expresses overall standing across every layer of visibility. This guide walks through both — the mentions-breadth metric and the weighted composite built on top of it.

But the formula hides the hard part. The inputs to AI share of voice are not the inputs to traditional SOV. You do not improve AI SOV by publishing more content, running more ads, or increasing your media spend. You improve it by making your brand the most credible, most extractable, most corroborated answer to the specific questions buyers ask AI engines.

That distinction matters because 38% of US online adults now use generative AI, with 62% of those users querying weekly. Over half have used AI specifically to find answers to questions — the exact behavior that determines whether your brand appears or doesn't. The audience is already there. The question is whether your brand shows up when they ask.

This shift is structural, not cyclical. Impression-based measurement assumed that visibility was a function of spend — the brand with the largest budget occupied the most attention. AI-mediated discovery inverts that assumption. A model deciding which brands to recommend evaluates evidence density, source diversity, and claim consistency across its entire retrieval context. No media budget influences that evaluation. The brands that win AI share of voice are the brands that have accumulated the most independent, verifiable evidence of their relevance to a given question. For marketing leaders still benchmarking against impression share, the gap between what they measure and what actually drives buyer decisions is widening every quarter.

Why Single-Platform Measurement Fails

Most teams checking their AI visibility open ChatGPT, type their brand name, and declare victory or defeat based on one response. This is the equivalent of checking one Google result on one device at one time of day and calling it your SEO strategy.

Research on GEO measurement shows that AI visibility varies across models, prompts, runs, and time. Your brand can be visible in one engine and absent in another for the same buyer question, which makes single-platform measurement structurally unreliable.

The operational cost of single-platform assumptions compounds over time. A marketing team that benchmarks only against ChatGPT and sees positive results may deprioritize investment in source architecture, not realizing that half their buyer audience is asking the same questions through Gemini or Perplexity, where the brand does not appear. Decisions made on incomplete data — budget allocation, content strategy, competitive positioning — drift further from reality with each measurement cycle. Multi-platform measurement is not a nice-to-have; it is the baseline requirement for any AI visibility strategy that claims to be data-driven.

The volatility goes deeper than platform differences. Schulte, Bleeker, and Kaufmann's research on GEO measurement demonstrated that answers vary across runs, prompts, and time even within a single platform. They characterize visibility as a distribution, not a data point — meaning a single query to a single model tells you almost nothing about your actual AI presence. Their recommendation: treat measurement as repeated sampling across multiple runs, not one-off observation.

This is why benchmarking matters. AI share of voice only becomes meaningful when measured against the brands that appear beside or instead of you. If your AI SOV significantly trails your real market position, you have a visibility problem that content alone will not fix.

The 30-to-1 Discovery Gap

The most important AI visibility study published this year tested 112 startups across 2,240 queries using ChatGPT and Perplexity. The results expose why AI share of voice is not just a marketing metric — it is a discovery mechanism.

When users searched by brand name, ChatGPT recognized products with 99.4% accuracy and Perplexity reached 94.3%. These brands exist in the training data. The models know they are real.

When users searched by category or problem — the way actual buyers search — ChatGPT's recommendation rate dropped to 3.32%. Perplexity reached 8.29%. That is a roughly 30-to-1 gap between being known and being discovered.

The finding that should reframe every visibility conversation: generative engine optimization (GEO), defined as optimizing website content specifically for AI, showed no correlation with actual discovery rates. What did correlate with Perplexity visibility were traditional authority signals — referring domains (r = +0.319, p < 0.001), community presence on Reddit (r = +0.395, p = 0.002), and overall web authority.

AI share of voice is not a content problem. It is an authority problem. The practical implication for marketing strategy is uncomfortable. Most teams respond to low AI visibility by producing more content — more blog posts, more landing pages, more optimized pages targeting specific queries. The evidence says this does not work. On-site content is a necessary condition for AI engines to understand what your brand does, but it is not a sufficient condition for them to recommend you. The sufficient condition is independent corroboration: other sources, other voices, other platforms confirming that your brand is a credible answer. Teams that double down on content production without investing in the source ecosystem around their brand are spending more to stand still.

The brands that AI engines recommend are the brands that multiple independent sources corroborate as credible answers. Content on your own website is necessary but not sufficient. The source ecosystem around your brand is what drives the recommendation. This finding should also reframe how brands evaluate their competitive position. A competitor with fewer products, less revenue, and a smaller team can hold higher AI share of voice if they have invested more heavily in the independent source ecosystem. Traditional competitive intelligence — market share, product features, pricing — does not predict AI visibility. The brands appearing in AI recommendations are the brands that third-party sources have independently validated as credible answers, regardless of their actual market position.

How AI Engines Select Sources (and Why It Matters for SOV)

Understanding how AI engines pick sources is the difference between measuring a vanity metric and measuring something you can actually influence.

Grossman and Liu's empirical study comparing Google Search, Gemini, and AI Overviews found less than 0.2 Jaccard similarity between the sources retrieved by traditional search and those retrieved by generative engines. In practical terms: the pages that rank on Google are largely not the pages that AI engines cite. The overlap is minimal.

Their research also revealed that 51.5% of representative user queries now generate AI Overviews, which appear above organic results. Google's AI Overview source selection uses a mechanism distinct from its ranking algorithm — 30% of AIO-cited sources do not appear in traditional first-page results. A separate measurement study across 55,393 trending queries confirmed that 13.7% of all queries trigger AI Overviews, with question-form queries activating at 64.7%.

The citation mechanism itself operates in two stages. Research from a 602-prompt cross-platform analysis separates citation selection (where a platform triggers search and chooses sources) from citation absorption (where a cited page contributes language, evidence, or factual support to the final answer). Perplexity and Google cite more sources on average, but ChatGPT demonstrates substantially higher average citation influence among fetched pages. Pages with high citation absorption share specific traits: greater length and structural organization, strong semantic alignment with the query, and rich extractable evidence including definitions, numerical facts, comparisons, and procedural steps.

The implication for AI visibility: your standing is not just a function of how often you are cited. It is a function of how deeply your content is absorbed into the answer. A brand cited by name but not absorbed into the reasoning is less visible than a brand whose evidence shapes the response. This depth is exactly what the composite AI Visibility Score captures beyond the mentions-based breadth of Share of Voice.

This two-stage model has direct consequences for measurement. Most AI visibility tools track mentions and citations — the first stage. Almost none track absorption — the second stage. A brand that publishes well-structured pages packed with definitions, proprietary data, comparative frameworks, and procedural steps will see its language woven into AI-generated answers even when the citation link is not displayed. A brand that publishes vague thought leadership will be cited but not absorbed. The absorbed brand shapes buyer perception. The cited-only brand is a footnote.

If the next decision is platform selection rather than measurement design, use AuthorityTech's AI search monitoring tools comparison to evaluate which tools support prompt libraries, cross-engine sampling, citation evidence, and source-level reporting.

To evaluate absorption, compare the language of the AI-generated answer against your source material. If the response uses your terminology, references your data, or follows your framework — even without an explicit citation — your content is being absorbed. If the response cites your page but uses none of your evidence, you have a citation without influence. Building toward absorption requires a fundamentally different content strategy than building toward mentions: fewer pages with deeper evidence density, not more pages with broader topic coverage.

How to Measure AI Share of Voice (The Operational Framework)

Here is the measurement framework that accounts for cross-platform variance, temporal volatility, and the distinction between mention breadth, citation depth, recommendation, and absorption.

Step 1: Define your query library, stratified by question shape. Identify 25-50 prompts that represent how buyers in your category search, and tag every one with its shape: best tools, how buyers choose, is it worth it, problem-first research, top lists, or comparisons. Those are the six shapes the Machine Relations Index reports separately, and the table above is why: the sources that win one shape mostly do not win the next. Include brand-specific queries ("What does [brand] do"), category queries ("best [category] tools 2026"), comparison queries ("[brand] vs [competitor]"), and problem queries ("how to solve [problem your product addresses]"), and keep enough prompts in each shape to give it a real denominator of its own. Segment prompts by intent stage — awareness, consideration, and decision — so your measurement captures the full buyer journey, not just top-of-funnel visibility. Design your prompt library to reflect how real buyers actually phrase questions, not how your marketing team talks about your product. AI users tend toward conversational, problem-first language — "how do I reduce customer churn with predictive analytics" rather than "best predictive analytics platform." Include prompts at varying levels of specificity. Broad category questions test whether your brand exists in the model's knowledge, while narrow problem-specific prompts test whether the model views your brand as a relevant solution. Refresh the library quarterly as language patterns and competitive landscapes shift.

Step 2: Test across platforms. Run every query through at least four engines: ChatGPT, Perplexity, Gemini, and one additional (Claude, Grok, or Google AI Mode). Treat each surface as its own retrieval environment: OpenAI documents ChatGPT search, Perplexity documents its search API behavior, Google has expanded AI Mode in Search, and Anthropic documents web search for Claude. Record whether your brand is mentioned, cited with a link, recommended as a top option, or used as a source without attribution.

Step 3: Measure repeatedly. Run the full query set monthly. Run a 10–15 query subset weekly. The measurement research is clear — single runs are unreliable. You need longitudinal data to separate signal from noise.

Step 4: Score by depth, not just presence. A mention is not a citation. A citation is not a recommendation. A recommendation is not source absorption. Track each level — these weighted levels are the inputs that compose the AI Visibility Score, while the mention level on its own is your Share of Voice:

Level	What It Means	Weight
Mention	Brand name appears in the response	1x
Citation	Brand is cited with a source link	2x
Recommendation	Brand is named as a top option or solution	3x
Source absorption	Brand's evidence, data, or framework shapes the answer	4x

Step 5: Calculate per shape, then benchmark. Your AI Share of Voice is the breadth layer — (your brand mentions) / (total brand mentions for all tracked brands) × 100 — and it is computed once per question shape, on that shape's own denominator, before any roll-up. Rolling in the weighted citation, recommendation, and absorption levels from Step 4 gives your composite AI Visibility Score = (your total weighted visibility) / (total weighted visibility for all tracked brands) × 100. Report the per-shape numbers beside the composite; a single blended figure will hide the shape where you are absent, which is usually the shape worth fixing. Compare either against your actual market share. A significant gap between market share and AI visibility signals that buyers who ask AI will not find you — regardless of your real-world position.

Two common measurement mistakes undermine this framework. The first is measuring too narrow a query set. Fifteen prompts all phrased in marketing language will confirm your biases, not reveal your visibility. The second is measuring without a competitive frame. Your AI share of voice as an absolute number is meaningless — it only matters relative to the brands that appear alongside or instead of you. Track at least your top five competitors and any emerging brands that appear unexpectedly. Unexpected competitors in AI results are often the earliest signal that your category is shifting.

Why Two Tools Report Different Share of Voice for the Same Brand

Buyers comparing vendors usually arrive at this question the same way: two platforms measured the same brand in the same month and returned different numbers, so one of them must be wrong. Usually neither is. Share of voice is not a property of your brand that a tool discovers; it is the output of a measurement contract, and the contract is the part vendors do not standardise.

Four choices move the number before any engine is queried, and no two products make them the same way:

Choice	Where vendors differ	Effect on the number
Prompt set	A fixed template library, a category keyword expansion, or prompts written from your own buyer decisions	Changes which question shapes are represented at all
Denominator	All tracked brands, all brands observed, or all mentions including unbranded	Changes the divisor, so the same numerator reports differently
Shape pooling	One blended figure across every prompt, or a figure per question shape	Pools markets with different leaders into one average
Mention rules	Aliases, product names and misspellings counted or not; repeated runs deduplicated or not	Changes the numerator

The pooling choice is the one that does the most damage and gets the least scrutiny, and it is measurable rather than theoretical. In the release read above, the AI Visibility & GEO category splits into six buyer question shapes, and no domain sits in the top three of all six — YouTube and Reddit each reach four, and every other leader changes with the question. A product that pools those six into one percentage is reporting the composition of its own prompt library. Give two vendors different prompt libraries over the same six markets and they will return different numbers for the same brand, both correctly, from the same underlying answers.

So the question to ask a vendor is not what your score is. It is which prompts, which denominator, whether the figure is pooled across question shapes, and what counts as a mention. A tool that cannot hand you those four answers has not measured your share of voice; it has reported one. The measurement contract sets out how to write those four decisions down before collection, which is also what makes two of your own measurement cycles comparable to each other.

What Actually Drives Sustainable AI Share of Voice

A 37,000-run audit across 533 brands stratified into five prominence tiers produced the clearest evidence of what drives — and limits — AI share of voice at scale.

Category leaders (L1) appear in nearly every relevant retrieval but win only 25–41% of recommendation slots they reach. Visibility is high, but differentiation determines conversion from visibility to recommendation.

Challengers (L2) show the strongest conversion rates of any tier at 37–52%, outperforming leaders. Their advantage: specific enough to match query intent, established enough to pass trust thresholds.

Mid-market brands (L3) hit the inflection point — aggregate coverage drops to 88% and conversion rates fall to 34–40%.

Specialists and regional players (L4–L5) face the harshest reality: 48–52% never surface in any of the 37,000 runs. They are effectively invisible to AI-mediated discovery.

The tier-specific evidence clarifies where to invest. Category leaders already have visibility; their challenge is converting that visibility into differentiated recommendations, which requires sharper positioning and more specific evidence of unique value. Challengers benefit from targeted campaigns that build corroboration in the specific niches where they outperform leaders — their conversion advantage comes from precision, not breadth. Mid-market brands face the most consequential strategic choice: invest heavily enough to cross the threshold into consistent retrieval, or accept that AI-mediated discovery will route buyers elsewhere. For specialists and regional players, the priority is establishing minimum viable presence in at least one engine through concentrated source-building in their specific domain.

The researchers' conclusion: "No uniform optimization recipe wins; the right marketing investment depends on where the brand sits on the prominence ladder." This is the opposite of what most AI visibility advice suggests. There is no single playbook. The question is not "how do I optimize for AI" — it is "what does my brand need to cross the credibility threshold where AI engines treat it as a legitimate answer."

For brands below that threshold, the evidence points to one lever that outweighs all others: third-party corroboration in sources that AI engines already trust. The Discovery Gap study showed that referring domains and community presence predicted Perplexity visibility more than any on-site optimization. The AI Visibility Lifecycle Framework, an IETF informational draft defining an 11-stage model from AI crawling through growth visibility, makes the mechanism explicit: crawlability does not equal visibility. A brand must pass through trust building, trust acceptance, and candidate surfacing stages before reaching any human-visible placement at all.

Why Earned Media Is the Compounding Layer for AI SOV

This is where most AI share of voice advice stops — at the measurement. Measure, benchmark, optimize your site. That is necessary. But it does not explain why some brands hold 35%+ SOV while others with more content, more pages, and more "optimization" never break 5%.

The answer is in the source architecture. AI engines do not generate answers from your website alone. They synthesize across every source they have access to — and they weight third-party sources that independently corroborate your claims higher than first-party claims you make about yourself.

Which third-party sources is the part worth measuring, because the famous mastheads do not behave as one class. In the Machine Relations Index release of 2026-10-04, across 20,821 monitored answer runs, Forbes was cited in 4.11% — first of 1,594 classified editorial publications — while TechCrunch was cited in 0.82%, the Wall Street Journal in 0.42% and hbr.org in 0.07%. That is a 59x spread across four names routinely listed together, and it widens on the exact question shape: on AI visibility and GEO comparisons, Forbes ranks #7 of 480 cited domains, the Journal #474 of 480, and TechCrunch and hbr.org are not observed in the category at all.

So when a buyer asks an AI engine who leads your category, the answer tracks your editorial presence in the publications those engines cite on that question. Not your ad budget. Not your blog volume. And not a masthead you assumed was in the answer without checking.

This is what Machine Relations — the discipline of earning AI citations and recommendations through third-party credibility — defines as the infrastructure layer beneath AI visibility. The mechanism: a brand earns a placement in a publication that AI engines index and trust. When a prospect asks about that brand's category, the AI cites the placement. The brand gets recommended through the same third-party credibility that made PR valuable in the first place — except the reader is now a machine.

The difference between a brand with 5% AI SOV and a brand with 40% AI SOV is rarely content quality. It is the number of independent, trusted sources that corroborate the same claims. Share of citation — the proportion of AI citations a brand captures in its category — compounds when the source ecosystem expands. It decays when the brand relies solely on owned content.

Discipline	Optimizes for	Success condition	Scope
SEO	Ranking algorithms	Top 10 position on SERP	Technical + content
GEO	Generative AI engines	Cited in AI-generated answers	Content formatting + distribution
AEO	Answer boxes / featured snippets	Selected as the direct answer	Structured content
Digital PR	Human journalists/editors	Media placement	Outreach + storytelling
Machine Relations	AI-mediated discovery systems	Resolved and cited across AI engines	Full system: authority → entity → citation → distribution → measurement

The practical implication: if your AI share of voice is lower than your market share warrants, the highest-leverage move is not publishing more blog posts. It is building the third-party source ecosystem — earned media placements, research citations, expert contributions, community presence — that gives AI engines multiple independent reasons to recommend you.

You can audit where your brand currently stands across AI engines with a visibility audit that maps your citation presence against competitors in your category.

What a credible AI Share of Voice report should contain

A credible report names its denominator. Ask for the prompt library, engines, run date, tracked competitors, and the rules used to classify a mention, citation, recommendation, or source absorption. Without those fields, two percentages called “AI share of voice” may not be comparable.

For a buyer or executive, the minimum useful report is:

• the exact category and buyer prompts tested, tagged by question shape
• a separate denominator, run count, and run-date count for each shape
• the engines and model surfaces tested
• your brand’s mentions and competitors’ mentions
• citation and recommendation counts kept separate from mentions
• the run date, repeat-sampling method, and material changes from the prior period
• the source evidence behind any claim that visibility changed

A vendor that gives you one score without its prompt set and denominator is giving you a dashboard number, not a decision instrument. The same test applies to a public index: the Machine Relations Index states its release id, window, observed runs, and run dates on every segment page, which is what makes one of its numbers comparable to the next.

You can read the public leaderboards for your own category at machinerelations.ai/index — it is Machine Relations' neutral instrument, free to read and cite, and it is the data authority behind the shape-stratified method on this page. AuthorityTech applies it to a specific brand: a visibility audit maps your citation presence against the competitive set on each shape and shows where the source ecosystem around your brand is thin.

FAQ

What is AI share of voice?

AI Share of Voice is the percentage of AI-generated responses that mention your brand relative to competitors for a defined set of prompts — a mentions-based breadth metric. Citation depth (Share of Citation) and the weighted roll-up (AI Visibility Score) sit alongside and above it. Unlike traditional SOV based on ad impressions, AI SOV reflects how AI engines evaluate your brand's credibility and relevance. Because GEO measurement varies across runs, prompts, and platforms, multi-engine measurement is essential.

Should AI share of voice be measured per question shape?

Yes. In the Machine Relations Index release mri_score_v2.1+2026-10-13+6fc79cb02bbf, the AI Visibility & GEO category splits into six buyer question shapes and no domain sits in the top three of all six; YouTube and Reddit each reach four. The shapes also carry different denominators, from 160 to 224 observed runs. A score pooled across shapes therefore reflects which shapes happened to be in the prompt library, not the brand's standing. Compute share of voice once per shape on that shape's denominator, then report the roll-up beside the six numbers.

How often should you measure AI share of voice?

Monthly full measurement across all tracked queries and platforms, with weekly spot-checks on your 10–15 highest-priority queries. Research on GEO measurement demonstrates that AI answers vary across runs, prompts, and time — single observations are unreliable. Quarterly strategic reviews should assess trends and inform whether your source architecture needs investment.

Why does my brand appear when searched by name but not by category?

This is the discovery gap. Research testing 112 startups across 2,240 queries found ChatGPT recognizes brands by name at 99.4% but recommends them in category searches at only 3.32% — a 30-to-1 gap. AI engines know your brand exists but lack sufficient third-party evidence to recommend it as a category answer. Closing this gap requires building the external source ecosystem that corroborates your relevance.

Who coined Machine Relations?

Jaxon Parrott, founder of AuthorityTech, coined Machine Relations in 2024 to define the discipline of earning AI citations and recommendations through third-party credibility. Unlike SEO (ranking algorithms) or GEO (generative engine formatting), Machine Relations addresses the full system — from entity authority through earned media placement to AI citation and measurement. The framework is documented at machinerelations.ai.