What 2,500 YC Startup Audits Reveal About AI Readiness
We audited every recent Y Combinator batch - W22 through W26. The data tells a story nobody in the ecosystem is talking about: the vast majority of funded startups are invisible to AI. Here is what the numbers say.
One of 53 criteria in AEO Rank, the citation-readiness score we run against every site we audit.
By Alex Shortov
Quick Answer
Across 2,500+ YC startups audited from 12 batches, the average AEO Site Rank is 38/100. Only 2% score above 70. Recent batches (W25, W26) score slightly higher - the ecosystem is waking up, but slowly. The two biggest, cheapest fixes are llms.txt adoption and FAQ content - both missing from 80%+ of startup sites and together responsible for the largest measured score lifts in our cohort.
Audit Note
In our audits, we've measured What 2,500 YC Startup Audits Reveal About AI Readiness on live sites, we've compared implementations, and we've audited the gaps that keep scores low.
How do YC startups score on AI visibility benchmarks?
Across 2,500+ YC startup audits the average AEO Site Rank is 38, only 2 percent score above 70, and recent batches edge ahead by 4-6 points as awareness grows.
What are the most common AEO gaps across funded startups?
Common gaps include missing llms.txt (over 80 percent of startups), default robots.txt with no AI rules, thin FAQ pages, no Organization schema, and declarative headings.
Which YC batch has the highest average AEO Site Rank?
Recent batches W25 and W26 post the highest average AEO Site Rank because AEO awareness is finally trickling into YC, though the overall ceiling stays in the low 40s.
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What this article answers
- How do YC startups score on AI visibility benchmarks?
- What are the most common AEO gaps across funded startups?
- Which YC batch has the highest average AEO Site Rank?
Key takeaways
- The average YC startup scores 38/100 on AI readiness - below the threshold where AI engines reliably cite a domain.
- llms.txt adoption is below 20% across all batches - the single easiest improvement most startups are skipping.
- Recent batches (W25, W26) show a 4-6 point higher average than older batches - awareness is growing but slowly.
- The top-scoring startups share three traits: llms.txt, 3+ schema types, and FAQ content with proper markup.
The Dataset Nobody Else Has
We audited 2,500-plus YC startups across 12 batches using the same 53 AEO Site Rank criteria, with an average score of 38 and a median of 35 across the population.
We have audited every publicly accessible startup website from 12 consecutive Y Combinator batches. W22, S22, W23, S23, W24, F24, S24, W25, SP25, S25, F25, W26. Over 2,500 individual domain audits using the same 53-criteria methodology across 5 pillars.
This is not a sample. This is the entire population of YC startups with active websites, scored on the same rubric, with the same AI engine, over the same time window.
The aggregate picture is sobering. Average score: 38 out of 100. Median: 35. That means more than half of all Y Combinator startups - the most well-funded, well-mentored startups in the world - are below the threshold where AI engines reliably discover and cite a domain.
This is not a funding problem. These companies raised money. It is not a talent problem. YC selects for elite technical founders. It is an awareness problem. Most startup teams simply do not know that AI visibility is a thing they need to build.
How Do AEO Site Ranks Trend Across YC Batches?
Scores trend slowly upward: older W22-S23 batches average 32-35, middle batches sit at 35-38, and recent W25-W26 batches reach 39-42 as awareness grows.
The data shows a clear but slow upward trend:
Older batches (W22-S23) average 32-35. These startups launched before ChatGPT reached mass adoption. AI visibility was not on anyone’s radar. Many of these sites still run default platform configurations with no AI-specific optimization.
Middle batches (W24-S24) average 35-38. Some awareness creeping in. A few more llms.txt files. Slightly more structured data. But still mostly accidental rather than intentional.
Recent batches (W25-W26) average 39-42. The trend is up. More startups are shipping llms.txt. More are adding schema. But 42 is still below the 50-point threshold where AI citation becomes reliable. The ecosystem is waking up, but slowly.
The sharpest signal? The gap between batches is smaller than the gap within batches. Every batch has startups scoring 70+ and startups scoring under 20. The batch does not determine the score. The founder’s awareness of AEO does.
What Are the Five Most Common AEO Gaps Across Startups?
Five recurring gaps drag startup audits: missing llms.txt, absent FAQ content, default robots.txt, single-schema homepage-only structured data, and weak entity authority signals.
Across 2,500+ audits, the same five criteria drag down startup scores:
1. No llms.txt (80%+ missing) The highest leverage-to-effort fix in the entire framework: 20 minutes of work, and it’s missing from over four in five YC startups. The weight inside the AEO Rank engine is modest by itself, but llms.txt also unblocks better crawling on every page below it - so the real impact extends well past the criterion’s own line item.
2. No FAQ content (75%+ missing) Startups build products, not knowledge bases. But FAQ content is the highest-density citation format. 15 questions with FAQPage schema creates 15 extractable answers. Most startups have zero.
3. Missing or default robots.txt (70%+ incomplete) Framework defaults do not mention AI crawlers. That is not hostility - it is silence. But silence is not a strategy.
4. No Q&A content structure (65%+ missing) Declarative headings (“Our Product”) instead of question headings (“How does [product] work?”). The content exists. The format does not match how people query AI.
5. Minimal schema markup (60%+ insufficient) Many startups have basic meta tags. Very few have Organization, FAQPage, or Article JSON-LD. Schema coverage below 2 types means most content is invisible to structured data consumers.
The pattern is consistent: these five gaps alone account for 35-40 points of potential score. Fix them and you jump from the bottom 40% to the top 20%.
The five most common AEO gaps across YC startups recur in the same order at the same severity.
| Gap | Prevalence | Score Impact |
|---|---|---|
| No llms.txt | Very common | Moderate |
| Thin Organization schema | Very common | Moderate |
| No FAQ page | Common | High |
| Generic, no original data | Universal | Severe |
| robots.txt blocks AI bots | Less common but severe | Severe when present |
What Do the Top 2% of Startups Have in Common?
Top 2% startups share three non-negotiable traits: structured detailed llms.txt files, three-plus stacked schema types, and FAQ pages with 20-30 real customer questions.
The top 2% of startup scores - above 70 - share three non-negotiable traits.
First, they all have llms.txt. Not a two-line placeholder. A structured, detailed file with product descriptions, team info, and content URLs. The AI has a complete picture before it even crawls a page.
Second, they run 3+ schema types. Organization is baseline. But the top performers add FAQPage, Article, WebSite, and often BreadcrumbList. Schema stacking compounds the provenance-trust signal - each additional type validates the others and lifts the criteria inside the provenance_trust cluster together.
Third, they invest in FAQ content. Not 5 generic questions. 20-30 real questions with 2-5 sentence answers. Proper FAQPage markup. Native HTML rendering. This is the content type with the highest citation-per-word ratio in our entire dataset.
What they do not share: budget for AEO agencies, dedicated content teams, or custom AI tooling. The top performers are technical founders who treated AI visibility like any other infrastructure requirement. Deploy it early. Iterate on it. Do not wait.
How Should Your Startup Use This Benchmark Data?
If you are a YC startup reading this, your competitive position in AI visibility is probably better than you think. Why? Because the bar is on the floor.
An average score of 38 means most of your batch mates are invisible to AI. Getting to 60 puts you in the top 10%. Getting to 70 puts you in the top 2%. These are not hard thresholds to cross - they just require awareness and a few hours of implementation.
The window is closing, though. Each batch scores a few points higher than the last. More accelerators are adding AEO to their playbooks. The startups that move now build an advantage that compounds over time. The startups that wait will face a harder climb when everyone else catches up.
We track every batch. We publish leaderboards. We run free audits for any YC startup. The data is there. The question is whether you use it.
Related topics
Key takeaways
- The average YC startup scores 38/100 on AI readiness - below the threshold where AI engines reliably cite a domain.
- llms.txt adoption is below 20% across all batches - the single easiest improvement most startups are skipping.
- Recent batches (W25, W26) show a 4-6 point higher average than older batches - awareness is growing but slowly.
- The top-scoring startups share three traits: llms.txt, 3+ schema types, and FAQ content with proper markup.