We measured "AI readiness" across 20,232 stores. Speed turned out to be completely irrelevant.

Spent the last few weeks crawling 20,232 Indian online stores, sampling up to 30 pages each, and scoring what a machine can actually read on them. Not rankings. Whether the page can be understood at all. Some of it surprised me, so sharing the numbers. The JavaScript thing is mostly a myth The usual explanation for AI invisibility is that crawlers can't handle JS-heavy sites. In this sample, 93% of stores serve their main content without JavaScript, and 99% aren't JS-only shells. Crawlers are getting in almost everywhere. Understanding is the problem, not access. Where it actually breaks (of stores with readable product pages) Marks the page as a product: 62% ...and attaches an offer: 60% ...and states a price: 56% ...and says whether it's in stock: 55% ...and has a product code: 42% So more than a third never signal "this is a product" at all. The one that changed how I think about it: 77% of stores show a price on the page. Only 56% declare it in markup. For roughly one store in five, a human can see the price and a machine can't state it. That's not a content problem, it's a template problem. Your platform decided this, not you Same check — does the product page declare itself a product: Wix: 97% Shopify: 68% WooCommerce: 63% Magento: 32% WordPress without WooCommerce: 11% Nobody picked their pass rate. They picked a platform years ago and inherited one. Related: 61% of stores serve a valid llms.txt, which sounds like huge adoption until you split it by platform. Shopify 98%, Wix 97%, WooCommerce 28%, PrestaShop 4%. The merchants didn't adopt anything, their platforms shipped it. Almost nobody has made a decision about AI crawlers 84.8% allow all of them 10.7% block every single one 0.3% block specific ones on purpose That 10.7% is almost always a blanket Disallow: / written for scrapers years ago, still sitting there, now excluding every assistant their customers ask. Speed was irrelevant I measured Core Web Vitals separately and kept it out of the score, then checked whether it correlated. Fast stores (PSI 50+): median readiness 72. Slow stores: median 72. Identical, not close. Speed matters for humans and for Google. For whether a machine understands what you sell, nothing. Top quarter vs bottom quarter, biggest gaps: offer attached (5% vs 93%), declared as product (7% vs 94%), stock status (5% vs 92%), price in markup (4% vs 90%), business identified (28% vs 95%). Seven of the eight largest gaps are product data. The eighth is business identity. None are speed, design or word count. What this doesn't measure: whether ChatGPT currently mentions any of these brands. That needs a prompt set run across assistants with answers recorded, which is a separate exercise. This measures readiness, not outcomes. It's also raw HTML only, no JS execution, and no backlink data. One thing that gave me confidence: the sample grew about sixfold while I was building it, from \~3,300 stores to \~19,700 scored. The median moved from 73 to 72 and the interquartile range stayed the same width. That stability suggests it's describing the market rather than whichever stores I happened to crawl first. Happy to answer questions on method or dig into specific cuts of the data.