What 2,500 YC Startup Audits Reveal About AI Readiness

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 22-criteria methodology across 4 dimensions.

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.

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.

Across 2,500+ audits, the same five criteria drag down startup scores:

1. No llms.txt (80%+ missing) The most impactful criterion by audit weight, and the easiest to implement. 20 minutes. Yet fewer than 1 in 5 YC startups have one. This single file accounts for 10% of the total score.

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 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. Claude's compound trust multiplier rewards this stacking - each additional type amplifies the trust signal from the others.

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.

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.