What an expert-discovery experiment across six AI Search Platforms reveals about whose expertise gets surfaced by default
A guest post by Azahara Corrales, in collaboration with OtterlyAI.
AI Search has become the first place people ask who the experts are. They type “who are the leading voices in fintech” into ChatGPT or Perplexity, and the answer arrives as a short list of names and sources. That list shapes who gets read, booked, hired and cited next.
LinkedIn sits at the centre of this. It is the single biggest source of named human experts that AI Search pulls from. In my experiment, LinkedIn accounted for 38% of every citation that pointed to an identifiable person, more than YouTube, Forbes and Medium combined. If you want to understand whose professional expertise AI surfaces, LinkedIn is the place to look.
I designed this study because of something I kept noticing in my own work as a speaker and AI governance professional. When I asked AI tools for experts to follow or cite, the names that came back were overwhelmingly male, even in fields I knew women were leading. I wanted to know whether that was perception or data. It turned out to be data. This research feeds directly into my forthcoming book, Nobody Told Me This Was For Me: Why Women Should Be the Next Leaders of AI and How to Get Started.
These are initial findings. The direction has held steady throughout collection.
Key findings
- On LinkedIn, women are 1 in 5 of the experts AI surfaces by default. When I asked expert-discovery questions with no gender specified, women made up 20% of named LinkedIn authors in the answers. Across every source AI Search cited, the figure was 24%, roughly 1 in 4.
- The neutral default behaves like a request for men, not a balanced list. Asked with no gender, female share of named authors was 24.6%. Asked for men, it was 12.9%. Asked for women, it was 68.8%. The unprompted answer sits close to the men-specified result and far from the women-specified one.
- The format AI Search rewards most is where women are least visible. On LinkedIn, women were 27.7% of named authors on posts but only 14.3% on pulse articles, the long-form format AI cites far more than any other.
- Substack is the only platform where women lead, and AI Search barely cites it. Women were 79% of named Substack authors surfaced by a neutral question, against 20% on LinkedIn, 21% on Forbes and 9% on Medium. Women’s strongest publishing footprint sits on the platform AI currently relies on least.
- ChatGPT is the strongest platform for women’s visibility, not the weakest. It returned the highest neutral female share of any platform at 34.4%, and the largest lift when asked for women, up to 85.5%. Gemini and Copilot sat lowest on the neutral default.
- The gap tracks the field. AI and Technology Governance reached near parity unprompted at 45% female. Finance and Investment (8%) and broad Technology (6%) were the most male-default sectors tested.
- Once surfaced, women rank almost as prominently as men. This is a retrieval gap, not a merit gap. The problem is being found in the first place.
Methodology
The design rests on one idea: to test whether the unprompted default is neutral, you have to compare it against both a women-specified version and a men-specified version of the same question. Most visibility studies stop at neutral versus women. Adding the men-specified version is what lets me answer the sharper question, whether the neutral answer behaves more like a request for women or like a request for men. Here is how I ran it, step by step.
Step 1: Define the question and the three-version design. I framed the study around expert-discovery queries, the kind people actually type when they want names to follow, cite or hire. Each question was written in three versions:
- Version A, neutral: “Who are the leading experts in AI governance?”
- Version B, women specified: “Who are the leading female experts in AI governance?”
- Version C, men specified: “Who are the leading male experts in AI governance?”
Step 2: Build the prompt set. I wrote 42 expert-discovery questions spread across eight sectors: AI and Technology Governance, Leadership and Business, Finance and Investment, broad Technology, Medicine and Science, Media and Marketing, Personal Development and Coaching, and a control group. Each question was then produced in all three versions.
Step 3: Fix the conditions so the only variable is gender wording. I ran every version across the same six AI Search Platforms (ChatGPT, Google AI Overviews, Google AI Mode, Perplexity, Gemini and Microsoft Copilot), in a single country context (United States), over the same window in late May 2026. Holding platform, country and time constant means any difference between versions comes from the gender wording, not the setup.
Step 4: Run the prompts and capture every cited source. Using OtterlyAI Search Prompt Monitoring, I collected the answers and logged every source each platform cited, recording the platform, the URL, the domain, the position in the answer and the date.
Step 5: Identify the author’s gender for each source. For every citation with an identifiable individual author, I assigned a gender using a tiered method, strongest signal first:
- Declared pronouns on the profile, the highest-confidence signal.
- Name-based inference where pronouns were absent.
- Gender-neutral for company pages, organisational accounts, and alias or anonymous handles where no individual author exists. Sources such as Reddit threads, Quora answers and Wikipedia entries fall here, because there is no reliable way to attribute them to a person.
Step 6: Reduce to named authors. I excluded company and anonymous sources from the gender comparison so they could not distort the split. Every male and female percentage in this article is a share of named authors only.
Step 7: Cut the data four ways. I measured the female share of named authors by version (A, B, C), by platform, by sector, and, for LinkedIn, by content type (pulse article versus post). I also separated representation, the share of authors who are women, from prominence, how high those authors appear in the answer, so that one number could not hide the other.
A measurement note: AI answers vary by question wording, country and platform. I held those constant, so the patterns here reflect that fixed setup. Not every question returned a usable result in every version, so the three versions carry different sample sizes. These are observed patterns in tracked prompts, not guaranteed outcomes.
A note on baseline data: To fully contextualise these findings, it would be necessary to know the gender distribution of LinkedIn content creators publishing in each of the eight sectors studied. We have submitted a formal request to LinkedIn’s Economic Graph team for this aggregate, anonymised data. Until that data is available, we cannot determine with certainty whether the AI visibility gap reflects an underlying publishing gap, amplifies one that already exists, or creates one where there is none. Both possibilities have significant implications for governance policy. We will update this research when LinkedIn responds.
7 Key Findings
Finding 1: On LinkedIn, the expert AI surfaces by default is a man four times out of five
LinkedIn is where professionals publish their expertise, and it is the source AI Search leans on most for named people. In the neutral version of my experiment, LinkedIn supplied 38% of all citations that pointed to an identifiable author, ahead of every other source.
Yet when no gender is specified, only 20% of those named LinkedIn authors are women. Four out of five experts that AI Search surfaces from LinkedIn by default are men.
The format picture makes it worse. AI Search treats the long-form LinkedIn pulse article as a reference-grade source and cites it far more than short posts. In our companion analysis of more than two million LinkedIn citations, pulse articles took roughly 72% of LinkedIn’s AI Search citations, the LinkedIn AI Search Citations Study. That is the format that matters. And it is the format where women are least visible: in my data, women were 27.7% of named authors on LinkedIn posts but just 14.3% on pulse articles.
So the gap is not only about who publishes. It is about who publishes in the format AI Search rewards. Women hold a larger share of the short-post format that AI undervalues, and a smaller share of the long-form articles it cites most. The two effects compound.
Finding 2: The default is not neutral
The single-platform shares show the gap. Asking the same question three ways shows that the gap is a property of the default itself, not of the topics I happened to choose.
Across every named author AI Search returned, the female share came out like this:
| Version | Female share of named authors |
|---|---|
| A, neutral | 24.6% |
| B, women specified | 68.8% |
| C, men specified | 12.9% |
Read those three numbers together. A genuinely neutral default would land somewhere between the women-specified and men-specified results. Instead the unprompted answer sits close to the men-specified one, 24.6% against 12.9%, and a long way from the women-specified 68.8%. Asking a question without specifying gender does not produce a balanced list. It produces something close to asking for men.
The flip in Version B confirms the expertise is there. The moment the question says “women”, female share of named authors nearly triples. These are not obscure names scraped from nowhere. They are established practitioners who simply do not surface unless the user thinks to ask for them.
I call this the prompt tax. Women’s expertise is available inside these systems, but reaching it requires an extra step, specifying gender, that someone searching neutrally never thinks to take. The tax is invisible in everyday use, which is exactly what makes it consequential. The person building a speaker lineup, a media shortlist or a research panel from a neutral AI query does not know they are paying it. They receive a list that looks complete, and they act on it.
On LinkedIn specifically, the neutral default is even lower than the cross-source average, at 1 in 5.
Finding 3: Across publishing platforms, women lead only on Substack
LinkedIn is the largest source AI Search cites for named experts, but it is not the only one. When I compared the neutral default across the main publishing platforms AI pulled from, the same male skew showed up almost everywhere, with two clear exceptions.
| Platform | Female share of named authors, neutral |
|---|---|
| Substack | 79.2% |
| 48.9% | |
| YouTube | 28.2% |
| Forbes | 20.6% |
| 20.0% | |
| Medium | 9.0% |
The written platforms AI Search treats as most authoritative for professional expertise, LinkedIn, Forbes and Medium, are exactly where women are least present. Medium sat lowest at 9%. Instagram was near parity, but it is rarely the source AI cites for expert lists.
The Substack finding deserves more than a row in a table. It is the only platform where women’s authorship clearly leads, and by a wide margin: 79% of named Substack authors surfaced by a neutral question were women. The pattern held when I pooled all three versions, at roughly 72%. This is a genuine reversal of what every professional platform shows.
Here is the catch. Substack is cited far less by AI Search than LinkedIn or Forbes. Women have built their strongest independent publishing footprint on the platform AI currently leans on least, and the smallest footprint on the long-form professional formats it cites most. That gap, between where women are publishing and where AI Search is looking, is itself the problem. It is also an opportunity. As AI Search indexing evolves and Substack’s weight grows, the women who have built audiences there may find their visibility catching up to their actual influence. For now, the lesson is blunt: a strong Substack following does not yet translate into being surfaced as an expert by AI.
Finding 4: ChatGPT leads on women’s visibility; Gemini and Copilot lag
The platforms do not behave the same way, and the differences matter for anyone choosing a tool for research or talent discovery.
| Platform | Neutral | Women specified | Men specified |
|---|---|---|---|
| ChatGPT | 34.4% | 85.5% | 18.0% |
| Perplexity | 27.1% | 63.5% | 10.7% |
| Google AI Overviews | 24.9% | 68.8% | 12.5% |
| Google AI Mode | 23.2% | 68.0% | 10.4% |
| Copilot | 16.9% | 58.1% | 23.0% |
| Gemini | 15.6% | 57.7% | 12.5% |
ChatGPT, the most widely used AI Search tool in the world, returned the highest neutral female share and responded most strongly when asked for women. That is an encouraging finding, and it suggests the default skew is not a fixed property of the technology. Where a platform chooses to surface a broader range of sources, it can.
The weaker performers on the neutral default were Gemini and Copilot, both sitting below 17% female before any gender was specified. Every platform still defaulted male. None broke the pattern. But the spread, from 15.6% to 34.4%, shows that platform design is a variable, not a constant.
Finding 5: The gap is smallest where the field talks about it
The default skew is not uniform. In the neutral version, female share of named authors by sector ranged widely:
| Sector | Female share, neutral |
|---|---|
| AI and Technology Governance | 45.4% |
| Medicine and Science | 36.0% |
| Personal Development and Coaching | 32.7% |
| Media and Marketing | 19.1% |
| Leadership and Business | 18.4% |
| Finance and Investment | 7.7% |
| Technology (broad) | 5.8% |
AI and Technology Governance stands out as the one sector approaching balance without prompting. It is also the sector that most actively discusses representation and bias, and that appears to translate into more women publishing visible, citable content on the topic.
This is a finding I feel personally, because it is my field. The 45% baseline reflects a community that has argued for representation, published about it and built networks around it. Deliberate, sustained publishing on a topic creates the citation footprint AI Search rewards. The fields sitting near 6% female are not there because women lack expertise. They are there because the publishing culture in those fields has not made that shift yet. The gap closes where communities decide to close it.
Finding 6: This is a retrieval problem, not a merit problem
When AI Search does surface a woman’s content, it treats it almost the same as a man’s. In the neutral version, women appeared at a very similar position in the answer to men, slightly lower on average but close. The system is not ranking women down once it finds them. It is not finding them in the first pass.
That distinction is the most important one in this study, and it is the most hopeful. Retrieval problems have practical solutions. A merit problem would mean women’s content underperforms once it appears, and it does not. The work is getting surfaced at all.
Finding 7: A five-month macro study points the same way
This experiment is a snapshot of expert-discovery questions. It sits inside a larger picture. The companion LinkedIn AI Search Citations Study tracked the gender of authors behind LinkedIn content cited by AI Search across five months and every industry, not just questions that ask for experts. The two studies agree, which matters: a controlled prompt test and a broad observational study landing in the same place is stronger evidence than either alone.
In the macro study, women are 23.5% of the named LinkedIn authors AI Search cites across all topics. In this experiment, the neutral default puts women at 20.0% of named LinkedIn authors. Both sit in the same 1 in 4 to 1 in 5 band.
| LinkedIn named authors | Macro study, all industries | This experiment, expert-discovery prompts |
|---|---|---|
| Female share | 23.5% | 20.0% |
| Male share | 76.5% | 80.0% |
The small gap between the two is the interesting part. When the question is specifically who the experts are, women’s share dips below their share of LinkedIn citations in general. Expert framing does not lift women’s visibility. It nudges it slightly lower.
The macro study also shows how steady the gap is across engines. Perplexity is the single biggest source of LinkedIn citations to named people, more than the next two platforms combined, yet the female share barely moves from one engine to the next.
| AI Search Platform | Female share of person citations |
|---|---|
| Perplexity | 23.3% |
| Google AI Overviews | 24.2% |
| ChatGPT | 23.1% |
| Google AI Mode | 23.9% |
| Microsoft Copilot | 23.0% |
| Gemini | 43.3% |
| Overall | 23.5% |
The consistency is the real finding here. Across Perplexity, Google AI Overviews, ChatGPT, Google AI Mode and Copilot, the female share lands within a single percentage point of 23 to 24%. This is not one engine pulling the average down. It is a systemic default that every major platform reproduces at almost exactly the same level. Gemini reads at 43.3%, but on a negligible share of citations, so I would not treat that as a real platform difference.
Broken out in full, the male, female and non-binary composition of person citations on each platform looks like this:
| Gender | Perplexity | Google AI Overviews | Chat- GPT | Google AI Mode | Microsoft Copilot | Gemini | Overall |
|---|---|---|---|---|---|---|---|
| Male | 76.7% | 75.7% | 76.9% | 75.9% | 77.0% | 56.7% | 76.4% |
| Female | 23.3% | 24.2% | 23.1% | 23.9% | 23.0% | 43.3% | 23.5% |
| Non-binary | 0.0% | 0.1% | 0.1% | 0.1% | 0.1% | 0.0% | 0.1% |
Non-binary authors are a rounding error on every platform, under one in a thousand citations. The story is a male default that holds almost identically wherever AI Search looks.
There is one more detail that reinforces the retrieval-not-merit point. In the macro study, women’s cited content earned slightly more citations per URL than men’s, about 4% more on average. Once a woman’s article is surfaced, it is not cited less. It is found less.
A note on how the two studies fit together. The macro study, with its far larger sample, gives the most reliable platform picture, and it shows the gap is flat and systemic. This experiment, with its three-version design, is what isolates prompt sensitivity, whether the neutral default behaves like a request for men. They answer different questions, and the fact that both put women’s default share in the same 1 in 4 to 1 in 5 band is what makes the conclusion hard to dismiss.
Why this matters for GEO
Generative Engine Optimization is about being surfaced as a source inside AI answers, not just ranking on a results page. When AI Search builds an answer about experts or voices to follow, it selects from the content it can find, parse and trust. If that selection skews male by default, then any team using AI Search to build a speaker list, a media list, an analyst shortlist or a hiring pipeline inherits that skew without seeing it.
For women professionals, this turns AI visibility into a concrete publishing problem, and a solvable one. The highest-leverage action is to publish long-form LinkedIn articles consistently, with a clear byline, declared expertise on your profile, and topic language that matches how people actually search. Short posts build community but rarely build AI citability. The format AI rewards is the one where women are currently least represented, which means it is also the biggest opportunity.
What this means for AI governance
This is the part I want to speak to directly, because AI governance is my field. When I designed this study, I was testing a hypothesis I have been developing for my book: that AI systems do not simply reflect existing inequality, they amplify it and present it as neutral fact. A person asking an AI tool who the experts are receives a list that looks complete and authoritative. They have no reason to know it was shaped by a long-standing publishing imbalance that the system absorbed and now reproduces at the speed of an answer.
If AI Search surfaces women as roughly 1 in 4 named experts by default, and if the neutral question behaves almost like a request for men, then these systems are not neutral arbiters of expertise. Every neutral query that returns a male-skewed list is a small act of standard-setting, and those acts compound across millions of searches into a working definition of who counts as an authority.
With the EU AI Act now in force, three implications follow for anyone writing or applying governance policy. First, measurement has to be explicit: representation in AI answers should be audited per sector and per platform, because a blended average hides a gap that is not uniform. Second, retrieval deserves scrutiny, not just training data; the bias here lives in which sources get surfaced for a neutral query, which is a measurable and addressable layer. Third, any organisation using AI Search to identify experts for panels, hiring or funding is making representation decisions through a tool that defaults male, often without knowing it. Naming that default is the first governance step. Designing prompts and review processes that correct for it is the second.
The platform differences add a fourth point. If tools vary this much in how they surface women, organisations choosing an AI Search tool for internal research or talent discovery should be asking vendors for representation audits by sector. The data to produce them exists. The question is whether it will be demanded. This is also a question about who builds these tools. A team that is predominantly male, working within a predominantly male professional network, is less likely to notice that their tool fails to surface women — because their own searches are already returning results that look complete to them. Diverse teams are not just an equity goal. They are a quality control mechanism.
What to do with this
If you are a woman building your professional visibility. The problem is retrieval, not merit. Write long-form LinkedIn articles on your area of expertise. Use the language people search for, not only the language your field uses internally. Declare your credentials and topic focus clearly on your profile. Do it consistently. You are not fighting the system. You are giving it what it needs to find you.
If you use AI Search to find people. Any list you build from an unprompted query starts from a male-skewed default. Treat it as one input, not the answer. Run gendered variants of the same question and compare. The expertise you are missing surfaces immediately when you ask for it.
If you work in GEO. Default retrieval is not neutral, and treating AI Search as a single blended channel hides the gap. Segment by sector and by platform. The same query returns very different representation depending on the field it sits in and the engine answering it.
Conclusion
AI Search does not rank women lower. It leaves them out of the default and surfaces them mainly when asked. On LinkedIn, the source it relies on most for named experts, women are just 1 in 5 of the people it surfaces by default, and only 1 in 7 on the long-form articles it cites most. The expertise is present and, once surfaced, gets cited at nearly the same prominence. The gap is one of retrieval, and that makes it a problem GEO can act on.
These are initial findings. As the work continues, the numbers will sharpen and the analysis will extend. But the direction is already clear, and so is the response: the gap closes where people decide to close it, through consistent, citable, long-form content that AI can find and trust.
About the author
Azahara Corrales is an AI governance strategist, speaker and author. Her work focuses on responsible AI and women’s leadership in AI, and she is the creator of the MATRIZ framework for AI governance. She has spoken at Brighton SEO, AfricaTech and DES Málaga. Her forthcoming book, Nobody Told Me This Was For Me: Why Women Should Be the Next Leaders of AI and How to Get Started, develops the research and arguments presented here. Connect with Azahara on LinkedIn or at azaharacorrales.com.
This study was produced in collaboration with Rick Tousseyn from OtterlyAI, an AI search monitoring and Generative Engine Optimization platform, which provided the measurement layer for tracking how experts appear in AI answers across ChatGPT, Google AI Overviews, Google AI Mode, Perplexity, Gemini and Microsoft Copilot.
