Where AI Gets Its Answers About Venture Capital (Q2 2026)

Highlights

• The dataset: The analysis behind this study covers over 180,000 AI source citations from prompts specific to the venture capital industry, with AI responses from four AI models run in 12 countries during Q2 2026.
• Reddit surged from #127 to #7: The biggest riser as investor databases lost ground in the back half.
• 9% of ChatGPT's citations are from reference domains: More than double any other model, driven by Wikipedia.
• 1% of Microsoft Copilot's citations are from user-generated domains: It almost never cites Reddit or forums.
• 60% of Google AI Overview's citations are from commercial domains: The most brand-direct model in the set.
• ~19% local-source rate in non-English markets: One of the lowest we've measured, because VC content lives on .com, .vc, and .io.
• 79% of top sources differ between any two models: Only 21% of top-cited domains overlap on average.

Which sources should you target to get cited as a venture capital brand?

Each model has a signature set of distinctive top sources.

A venture-capital brand optimizing for one AI model is absent from roughly 79% of the domains the other models prefer.

How have AI source rankings changed over time in the venture capital industry?

Splitting the ~15.8-week window at its midpoint (4 May 2026) and comparing each domain's citation share in the first half against the second surfaces large movements among the top sources.

The headline reversal is community versus database. Reddit climbed from about #127 to #7 in most-cited domains, growing its citation share more than tenfold (from roughly 0.15% of citations to 1.78%), while the investor databases and directories that dominated the first half lost ground: PitchBook fell about 122 places in share rank, Crunchbase about 58, Tracxn about 221, and InvestorHunt about 150. On the way up alongside Reddit: descubre.vc climbed about 186 places, reuters.com about 171, cincodias.elpais.com about 143, and aspireapp.com about 142.

What type of content do AI models cite for venture capital firms?

We classified every cited domain by category (commercial, editorial, user-generated, reference, institutional) using the global domain registry. The mix differs by model, though venture capital tilts commercial across the board, a reflection of how much of this space is fund websites, pitch-deck tools, and directory products.

Google AI Overview is the most brand-direct, sending 60.4% of its citations to commercial domains, it will quote the fund's or product's own page. ChatGPT is the least commercial at 43.5%, spreading instead into reference content (9.3%, more than double any other model, driven by Wikipedia) and user-generated content (7.7%). Microsoft Copilot is a near-opposite of ChatGPT on the community axis: only 1.3% of its citations touch user-generated content like Reddit or forums, the lowest in the set, while it leans harder on editorial comparison media (28.9%).

Do AI models cite local-language content for venture capital firms?

Every English-language prompt in the dataset (US, UK, Canada, Australia) returned sources that were entirely on English or generic domains, local-country ccTLDs accounted for 0% of citations there.

For non-English markets we use a country-code top-level-domain (ccTLD) proxy: a German-language prompt's citation "counts as local" if the domain ends in .de, a French one if it ends in .fr, and so on (for Spanish we count .es, .mx, and .ar). Across all non-English prompts, only 19.4% of citations went to a local ccTLD, one of the lowest local rates we have measured, and far below the ~50% we see in wealth and investment management. The reason is structural: venture-capital content overwhelmingly lives on generic domains, .com, .vc, .io, .app, so even a German-language answer about German funds gets pulled toward international startup directories.

Italian, German, and French venture questions stay somewhat local; Swedish and Dutch questions are pulled hard toward global .com/.vc directories, and Spanish questions fragment their "local" signal across three ccTLDs (Spain, Mexico, Argentina). This is a ccTLD proxy, not true language detection, a .com domain can publish in German, so these figures understate genuine local-language coverage.

Which AI model relies most on local sources for venture capital firms?

The localization gap between models is real and consistent. Reading the same languages model by model, Google AI Overview is the heaviest user of local sources in most markets, while Grok most often defaults to global English-language and generic domains, most visibly in Dutch, where only 2.6% of Grok's citations carry a local ccTLD.

Two patterns stand out. Google AI Overview relies on local sources the most, around half its citations are local in German (51.4%) and Italian (52.2%), while Grok cites local sources sparingly everywhere, rarely climbing above 22%. Google AI Overview returned no cited sources for French-language venture-capital prompts in this window, so that cell is blank rather than zero. Swedish is the hardest market for every model: even the model that relies most on local sources clears just 29.3%, because Swedish venture content sits mostly on .com and pan-Nordic directories.

Should venture capital firms optimize for each AI model separately?

Yes, mostly. We built each model's most-cited domains for venture capital and compared every pair to see how many sources they share. The average overlap is 20.7%, meaning 79.3% of top-cited sources differ between any two models. That is higher than in many industries we measure, because a small set of startup-fundraising directories appears across all four models, but it still leaves roughly four out of five sources unshared between any pair.

The spread is wide. Grok is the closest cousin to both Google AI Overview and Microsoft Copilot, it shares 8 of its top domains with each (25.0% overlap). At the other extreme, Microsoft Copilot and Google AI Overview share just 14.3%, five domains out of the combined set, with Microsoft Copilot leaning on regional and comparison portals like infocession.fr, vcgermany.de, and startupsavant.com while Google AI Overview prefers curated fund lists like openvc.app, growthmentor.com, and beauhurst.com.

How many sources does each AI model cite per answer?

The models differ by more than 4x in how many sources they cite in a single answer.

Grok cites 31.0 sources per response, about 4.4x Microsoft Copilot's 7.1, and a single Grok answer reached as many as 364 cited sources. Google AI Overview and ChatGPT sit lower (about 8 and 6), though ChatGPT is the most variable, sometimes citing a single source and sometimes dozens. For a firm, Grok offers many more slots per answer but each carries less weight, while ChatGPT's and Microsoft Copilot's short citation lists 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 venture-capital prompts, choosing, comparing, and recommending venture firms, seed and early-stage investors, accelerators, and fundraising resources, across 12 countries and 7 languages between 10 March and 29 June 2026.

It describes which sources AI models cite, not whether those sources are accurate or whether the recommendations are good. A citation is not an endorsement. These findings reflect citation behavior for venture-capital prompts in the monitored markets, which skew toward English-speaking and Western European/Latin American markets, and may not generalize to all firms, prompt types, or other verticals.

Methodology

How we measured this

We collected the answers four AI models gave to venture-capital prompts across 12 countries and 7 languages over roughly 15.8 weeks, then extracted every web source each model cited. Domain categories (commercial, editorial, UGC, reference, institutional) were assigned from a global domain registry. Cross-model overlap was measured on each model pair's top-20 most-cited domains, and reported as the mean overlap plus the full range across the six model pairs. Temporal analysis split the observation window at its midpoint (4 May 2026) and compared each domain's citation share in each half.

The analysis rests on over 180,000 cited sources from 17,336 AI responses, above the thresholds our research protocol requires for proportion and trend estimates. Localization is measured by a country-code-domain proxy (the source records carry no detected content language), so those figures are directional and exclude English-language prompts, which sit overwhelmingly on generic domains. Model coverage varied across the observation window, so temporal movements are reported as relative ranks within each half.

Frequently asked questions

Do different AI models really cite different sources for the same venture-capital question?

Yes, though less extremely than in some industries. Any two models share only about 21% of their top-20 cited domains, meaning roughly 79% of the sources one model relies on are absent from another's top list. A shared core of startup directories (awisee.com, basetemplates.com, shizune.co, vc-mapping.gilion.com) appears in all four, but beyond it the lists diverge fast.

Which model should a venture firm or fundraising founder prioritize?

It depends on the content you can produce. Google AI Overview rewards your own commercial pages and placement on curated fund lists (60.4% of its citations are commercial). Microsoft Copilot rewards third-party comparison and editorial media (28.9% editorial, and almost no community content). ChatGPT rewards community presence and reference authority, 9.3% of its citations go to reference sites like Wikipedia and another 7.7% to user-generated content like Reddit.

Does writing in the local language help?

In non-English markets, a little, but less than in most industries. Across non-English prompts, only about 19% of citations went to a local-country domain, because venture content lives overwhelmingly on generic .com/.vc/.io domains. Italian, German, and French questions stay somewhat local (25–30%), while Swedish (10%) and Dutch (15%) skew hard toward global directories.

Which model relies most on local sources?

Google AI Overview relies on local sources the most, around half its citations are local in German (51.4%) and Italian (52.2%). Grok relies on local sources least, defaulting to global directories, with Dutch its weakest market at just 2.6% local.

How many sources does each model cite per answer?

Grok cites the most by far, about 31 sources per response, with some answers exceeding 360. ChatGPT cites the fewest at about 6. That near-5x 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, Reddit climbed from around #127 to #7 while investor databases like PitchBook, Crunchbase, and Tracxn fell 58–221 places in share rank. AI source rankings churn fast, so visibility has to be defended continuously.

Why do investor databases like Crunchbase and PitchBook matter so much?

They are central to Grok, which floods its answers with investor databases and startup directories, Crunchbase, PitchBook, Tracxn, CB Insights, and OpenVC all sit near the top of its list. For a firm, maintaining accurate, complete profiles on these databases is the most direct lever for Grok visibility, distinct from the plays that move the other models.