Maybelline has been in medicine cabinets since 1915. Fenty Beauty launched in 2017. On any traditional distribution metric, Maybelline should crush this comparison: more SKUs, more retail shelves, more Google search volume, more decades of ad spend. It's not close.
Then we asked three AI models which cosmetics brands people should buy right now. Fenty won 2-0. Maybelline didn't take a single model.
You can pull up the full matchup here: foxish.ai/vs/fenty-vs-maybelline. But the short version is a Fenty sweep, and the reasons why are the story every legacy brand should be paying attention to.
The Matchup and Why It Matters
Fenty vs Maybelline is the cleanest possible test of "legacy vs modern" in cosmetics. Maybelline is the drugstore standard-bearer with 100+ years of brand equity, a global L'Oréal-backed distribution machine, and category-defining hero SKUs like Great Lash and Sky High mascara. Fenty is nine years old, celebrity-founded, DTC-native, and famous for reshaping the shade-inclusivity conversation with its 40-shade (now 50+) foundation launch.If AI models were just parroting historical brand strength, Maybelline should show up more. It doesn't. Which means the models are indexing on something else, and figuring out what that "something else" is has direct revenue implications for anyone selling makeup online.
We ran five buyer-shaped prompts through ChatGPT, Gemini, and Perplexity. Things like "best cosmetics brands to buy right now" and "top makeup brands worth trying in 2025." Then we counted mentions per brand, per model.
Here's what came back.
The Scoreboard
| Model | Winner | Fenty mentions | Maybelline mentions |
|---|---|---|---|
| ChatGPT | NARS (neither) | 4 | 1 |
| Gemini | Fenty | 2 | 1 |
| Perplexity | Fenty | 4 | 3 |
Gemini was the tightest on volume (2 vs 1) but still leaned Fenty. Perplexity produced the closest fight (4 vs 3), which tracks with Perplexity's tendency to cite a wider spread of sources and surface more brands per answer.
The interesting thing isn't just that Fenty won. It's that the newer, smaller, less-distributed brand won across three models with very different training pipelines and retrieval strategies. That's not a coincidence. That's signal.
Fenty won because it looks the way AI models want cosmetics brands to look. Maybelline still looks the way Google wanted brands to look in 2010.Why the AI Picked Fenty
We can't crack open the weights and look inside, but we can reason about what these models are trained on and what they retrieve at inference time. Four things are almost certainly driving the outcome.
Fenty dominates the "why it matters" corpus. When journalists, bloggers, and Reddit threads talk about Fenty, they talk about the story: the 40-shade launch, Rihanna, the industry-wide shade expansion that followed. That narrative gets restated constantly. Every "brands that changed the beauty industry" listicle since 2018 mentions Fenty. Every diversity-in-beauty explainer mentions Fenty. LLMs trained on that corpus have absorbed a very tight association between "Fenty" and "important cosmetics brand you should know."Maybelline's story in the training corpus is more diffuse. It shows up in "best mascara" roundups, "drugstore dupes" articles, and product review aggregations. It's present in the training data — nobody's arguing otherwise — but it's present as a commodity brand, not as a recommendation-worthy answer to "what should I buy right now."
Fenty's review corpus is denser and more structured. Sephora reviews, Ulta reviews, YouTube tutorials, Reddit r/MakeupAddiction threads. When models retrieve reviews at inference time (which Perplexity does aggressively and ChatGPT does via search), they hit rich, opinion-heavy content on Fenty. Maybelline's reviews skew shorter, older, and more transactional. A model looking for content to justify a recommendation finds more usable material on Fenty per query. Product breadth vs product hero problem. Maybelline's brand is anchored to specific hero SKUs (Great Lash, Fit Me, Sky High). When you ask about mascara specifically, Maybelline shows up hard. When you ask about "brands to buy," the model needs a brand-level narrative, and Fenty's brand-level narrative is stronger than Maybelline's brand-level narrative, even if Maybelline's product-level presence is bigger. Recency bias. Fenty has had continuous product launches and press coverage from 2017 to now. Maybelline's biggest recent news beat was Sky High mascara going viral on TikTok in 2021. In training data terms, Fenty produces a steadier drumbeat of fresh content. Models weight recent, active brands more heavily when the query is present-tense ("buy right now").Notice what's not on this list: ad spend, retail distribution, price point, or SKU count. The AI answer layer doesn't care about the metrics that made Maybelline the market leader in the first place.
What Maybelline Could Actually Do About It
If you're on Maybelline's brand team (or you're running a legacy brand with a similar problem), generic marketing advice won't fix this. You need AEO moves — Answer Engine Optimization — that target the specific signals these models are reading.
Build brand-level narrative content, not just product content. Maybelline's owned content is heavily product-focused: how to apply Sky High, how to pick your Fit Me shade. What's missing is brand-level editorial that answers questions like "why has Maybelline been the #1 mascara brand for X years" or "what makes Maybelline different from other drugstore brands." This content seeds the training corpus and the retrieval corpus with brand-level narrative that models can quote when asked brand-level questions. Get into the "brands that matter" corpus, not just the "products that work" corpus. Fenty is in every industry-shaping-brands roundup. Maybelline needs to be there too. That means PR pushes targeted at the exact types of publications (Business of Fashion, Glossy, Beauty Independent, Vogue Business) whose content gets scraped into training sets and cited by Perplexity. It's not "get press coverage." It's "get press coverage of a specific kind, in a specific voice, in specific outlets." Seed Reddit and forum content deliberately. LLMs and retrieval systems love Reddit. Maybelline shows up in r/MakeupAddiction and r/drugstoreMUA but often as a "budget alternative" framing, not as a first-choice recommendation. Brands that engage with communities authentically (not through obvious astroturfing, which now gets sniffed out by the models themselves) accumulate the kind of unstructured social proof that shows up in retrieval. Own a defensible AEO position on category-defining queries. Maybelline has a real story to tell about accessibility, drugstore excellence, and mass-market innovation. That story isn't being told in machine-readable form. Structured content — FAQ pages, comparison pages, category explainers on maybelline.com — that directly answers "why choose Maybelline" gets crawled, indexed, and often quoted verbatim by AI systems. Fix the shade-inclusivity narrative gap. Fenty owns "inclusive shade range" in the AI corpus. Maybelline's Fit Me line actually has strong shade coverage but nobody's telling that story with the same intensity. Every time an AI answers "inclusive foundation brands" without saying Maybelline, that's a repairable content gap. The point isn't to become Fenty. The point is to make sure the AI has something to say when someone asks about Maybelline.The Broader Takeaway for Cosmetics Brands
Fenty and Maybelline are two data points in a much bigger pattern. If you're running a cosmetics brand — Charlotte Tilbury, Rare Beauty, Merit, Glossier, Ilia, Saie, any of them — this matchup tells you something specific about how the answer layer is going to treat you.
Brand-story-strong brands are winning. If your brand has a clear origin story, a founder narrative, a category-shaping moment, or a strong point of view, AI models will surface you more readily. Rare Beauty (Selena Gomez, mental health), Merit (minimalist beauty, clean formulations), Ilia (clean beauty pioneer) — these brands have compressed narratives that models can restate in a sentence. That matters. Product-strong-brand-weak brands are losing. If your brand is a collection of good SKUs without a unifying story, you'll show up in specific product queries but disappear from brand-level recommendation queries. Those brand-level queries are where the discovery volume lives. Legacy distribution doesn't transfer. Being at every Walgreens, Target, and CVS in America is worth roughly nothing in the AI answer layer. The models don't know or care where you're physically stocked. What they care about is where you show up textually — in reviews, articles, forums, and structured content. Mention volume is the leading indicator. In our data, Fenty won on mention count in every model, even the one where it wasn't the top pick. Mention count is the earliest signal you'll get that you're gaining or losing ground. By the time you drop out of "top pick" position, you've probably been losing mention share for months.Which is exactly the thing most brands aren't measuring.
What This Means for Your Brand
Every cosmetics brand should assume that a meaningful and growing share of purchase discovery is happening inside AI chats. Not Google. Not TikTok. Chat interfaces where the customer asks "what should I buy" and the AI answers with a shortlist of two or three brands.
If you're not on that shortlist, you don't exist for that customer.
The good news: this is measurable. You can see exactly which prompts you're winning, which ones you're losing, and to whom. You can see your mention share by model. You can watch it move week over week. You can A/B test AEO changes and see the impact in your visibility scores. The uncomfortable news: most brands aren't measuring any of this yet. Which means the ones that start now build a lead their competitors won't be able to see, let alone close, for another 12-18 months.Fenty didn't win this fight because it spent more on ads than Maybelline. It won because its content, narrative, and corpus presence match what the models are looking for. That's a solvable problem for any brand willing to treat it as one.
Run Your Own Fight
If you want to see how your brand stacks up against a competitor across ChatGPT, Gemini, and Perplexity, we built the /vs tool specifically for this. Pick any two brands, get a full head-to-head with per-model breakdowns, mention counts, and the verdict. It's the same methodology we used on Fenty vs Maybelline. Try it at foxish.ai/vs.
And if you want to see your brand's full AI visibility profile — every prompt you're winning, every prompt you're losing, and where the gaps are — Start a free scan →. First scan is on us, no card required.