Private Equity AI Visibility Report: Which Domains Get Cited (Q2 2026)

Highlights

• The dataset: We analyzed over 180,000 AI source citations generated from prompts specific to the private equity industry. The AI responses behind them come from four AI models and were run in 12 countries over the course of Q2 2026.
• 13% of ChatGPT's citations are from reference domains: Wikipedia and similar, more than four times any other model.
• privateequitylist.com sits in every model's top 6: The closest thing to a universal PE citation.
• 57% of Google AI Overview's citations are from commercial domains: The most brand-direct model, it will quote a firm's own page or a PE listing site.
• ~28% local-domain citation rate in non-English markets: English-language markets vary widely, near 0% in the US but roughly 12% to 30% in the UK, Canada, and Australia.
• 19% average overlap between any two models: Any two of the four share only about a fifth of their top-20 cited private-equity domains.

Which sources should you target to get cited as a private equity brand?

The character of each model's top sources differs as much as the lists themselves; the leading cited sources here all appear in Temso's publisher directory.

How have AI source rankings changed over time in the private equity industry?

We split the ~15.8-week window at its midpoint (4 May 2026) and compared each domain's citation share in the first half against the second. We compare shares rather than raw counts and read the movement as directional.

The steepest falls are the professional league tables: chambers.com dropped out of the top 50 entirely (from #6), privateequityinternational.com fell from #5 to #21, pitchbook.com fell from #9 to #31, and Dakota slipped from #1 to #5. On the way up: the Italian PE association aifi.it climbed from #42 to #10, the deal-finder rainmakrr.com climbed from #20 to #2, and Reddit entered the top table at #11.

What type of content do AI models cite for private equity firms?

We classified every cited domain by category. Every model here is commercial-heavy, this is a directory-and-firm-page industry, but the secondary mix separates them clearly.

Google AI Overview is the most brand-direct, sending 56.7% of its citations to commercial domains. Microsoft Copilot is the most editorial, with 27.7% going to deal-comparison and review media, the most of any model. ChatGPT is the reference model: 12.7% of its citations go to reference content like Wikipedia, more than four times any other model, and it cites the least user-generated content (4.9%). A residual of citations, 4.0%–8.2% per model, hit domains not yet categorized in the registry. For a firm, the optimization target changes per model: own-site content and directory listings move Google AI Overview; deal-comparison portals move Microsoft Copilot; a strong Wikipedia footprint and press coverage move ChatGPT.

Do AI models cite local-language content for private equity firms?

Local-country domains play very different roles across the English-language markets: the US sits near 0% (0.04%), while the UK (11.7%), Canada (13.9%), and Australia (29.6%) all carry meaningful local-ccTLD shares. Using country-code top-level domains as a proxy for local-language sourcing, only 28% of citations in non-English prompts went to a local-country domain, lower than in most industries, because private-equity questions pull heavily toward global .com directories.

French and German private-equity questions stay the most local; Spanish is pulled hardest toward global .com directories, unsurprising given the Spanish prompts span Spain, Mexico, and Argentina, fragmenting the "local" signal across three country domains. Even the most local language here (French, 39.9%) sits below where the top languages land in most other industries. This is a country-domain proxy, not true language detection, so these figures understate genuine local-language coverage and should be read as a directional floor.

Which AI model relies most on local sources for private equity firms?

The localization gap between models is real and consistent. Microsoft Copilot and Google AI Overview lean hardest on local sources, while Grok almost always defaults to global English-language and .com directories, most starkly in German, where only 10.2% of Grok's citations carry a local-country domain.

Microsoft Copilot relies on local sources the most in German (76.1%) and French (66.6%), while Google AI Overview reaches 75.3% local in Dutch. Grok is the consistent outlier at the bottom, citing local domains barely a tenth of the time in German and Dutch and staying under a quarter almost everywhere. Google AI Overview returned no cited sources for French-language private-equity prompts in this window, so that cell is blank rather than zero. For a firm marketing across Europe, local-language content pays off on Microsoft Copilot and Google AI Overview but does almost nothing for Grok visibility.

Should private equity firms optimize for each AI model separately?

Mostly yes, but with one shared shortcut. When the four models answer the same private-equity questions, their top-cited domains barely converge. We compared the 20 most-cited domains for each model and measured how much they share. The average overlap is 19.1%, meaning 80.9% of top-cited sources differ between any two models, higher overlap than we see in most finance verticals, because a thin layer of PE-specific directories is cited by everyone.

Grok and Google AI Overview are the closest cousins, sharing 10 top domains (33.3%). At the other extreme, ChatGPT shares just 14.3% with both Grok and Microsoft Copilot, five domains out of forty.

A two-proportion test confirms the 19.1%-overlap versus 80.9%-differ split is far from chance. There is no single "rank #1 in AI" to win, there is a shared directory layer plus four separate citation economies stacked on top.

How many sources does each AI model cite per answer?

The models differ by about 4x in how many sources they pull into a single answer.

Grok cites 29.0 sources per response, about 4.0x Microsoft Copilot's 7.2, and a single Grok answer reached as many as 158 cited sources. The other three cluster far lower: Google AI Overview at 9.0, Microsoft Copilot at 7.2, and ChatGPT at 6.9, the fewest of the four. For a brand, Grok offers many more slots per answer but each carries less weight, while the short citation lists on ChatGPT and Microsoft Copilot make every included source disproportionately valuable.

Context

This analysis draws from Temso's AI visibility monitoring platform, which tracks how brands appear in AI model responses across ChatGPT, Microsoft Copilot, Grok, and Google AI Overview. The dataset covers private-equity and alternative-investment prompts, choosing, comparing, and recommending PE firms, buyout houses, growth-equity investors, and alternative-asset managers, across 12 countries and 7 languages between 10 March and 29 June 2026.

It measures which sources AI models cite, not whether those sources are accurate or whether the recommendations are good. "Citations" are the web pages a model referenced; a citation is not an endorsement. The data is observational and weighted toward English-speaking and Western European/Latin American markets. Where a model produced few or no cited sources for a given market (Google AI Overview in French), we flag the gap rather than infer a rate from thin data.

Methodology

How we measured this

We tracked four AI models responding to private-equity and alternative-investment prompts across 12 countries and 7 languages over roughly 15.8 weeks. Each response was parsed to extract source citations: the URLs, domains, and metadata referenced. The analysis rests on over 180,000 cited sources from 17,537 AI responses, comfortably above the thresholds our research protocol requires for proportion and trend estimates. Domain categories (commercial, editorial, UGC, reference, institutional) were assigned from a global domain registry. Cross-model overlap was measured using Jaccard similarity on each model pair's top-20 most-cited domains, reported as the mean overlap with the full range across the six pairs. Temporal analysis split the observation period at its midpoint and compared each domain's citation share in each half. Model coverage varied across the observation window, so temporal movements are reported as relative shares within each half.

Localization is measured with a country-code-domain proxy (the source records carry no detected content language): a citation "counts as local" when the domain's country code matches the prompt's market. Those figures are directional and exclude English-language prompts, which sit overwhelmingly on generic domains.

Frequently asked questions

Do different AI models really cite different sources for the same private-equity question?

Yes, though less extremely than in most finance verticals. Any two models share only about 19% of their top-20 cited domains, meaning roughly 81% of the sources one model relies on are absent from another's top list. A shared layer of PE directories, privateequitylist.com, Dakota, Leaders League, Clutch, is the exception that appears everywhere.

Which model should a PE or alternative-investment firm prioritize?

It depends on the content you can produce. Google AI Overview rewards your own commercial pages and directory listings (56.7% of its citations are commercial). Microsoft Copilot rewards third-party deal-comparison and review media (27.7% editorial, the most of any model). ChatGPT rewards reference authority and press, 12.7% of its citations go to reference sites like Wikipedia, over four times any other model.

Is there any source that works across all four models?

Yes, the PE directory layer. privateequitylist.com sits in every model's top six, and Dakota, Leaders League, and Clutch recur across most lists. Earning a strong, accurate profile on these specialist directories is the rare AI-visibility lever that pays off on every model at once.

Does writing in the local language help private equity AI visibility?

In non-English markets, somewhat, but less than in other industries. Across non-English prompts, only about 28% of citations went to a local-country domain, because PE search leans on global .com directories. French and German questions stay the most local (~38–40%), while Spanish skews toward global portals (~19%). Among English-language markets the picture splits: the US pulls almost no local-domain sources (near 0%), while the UK, Canada, and Australia run from about 12% to 30%.

Which model relies most on local sources for private equity?

Microsoft Copilot relies on local sources the most in German (76.1%) and French (66.6%), and Google AI Overview reaches 75.3% local in Dutch. Grok relies on local sources least by a wide margin, barely a tenth of its German and Dutch citations carry a local-country domain, defaulting to global directories almost everywhere.

How many sources does each model cite per answer?

Grok cites the most by far, about 29 sources per response, with some answers exceeding 150. ChatGPT cites the fewest, at about 7, just below Microsoft Copilot. That roughly 4x gap means each source in a ChatGPT or Microsoft Copilot answer carries far more weight than one of Grok's many citations.

Are AI citation rankings stable over time?

No. Over ~15.8 weeks, professional league tables like Chambers, PEI, and PitchBook slid in the ranking while directory and reference sources rose, a directional signal rather than a proven re-ranking. AI source rankings churn fast, so visibility has to be defended continuously.

Can these numbers tell me whether a recommendation is accurate or trustworthy?

No. This study measures which sources models cite, not whether those sources are correct or whether the recommendation is sound. A citation reflects what a model surfaced, not an endorsement of quality.