Only 8.9% of sites block AI crawlers, but 94.8% are never cited in AI answers(website-auditor.io) |
Only 8.9% of sites block AI crawlers, but 94.8% are never cited in AI answers(website-auditor.io) |
Let’s say I ask “who created Linux?” Claude correctly tells me Linus Torvalds, and links to Wikipedia.
There are probably thousands, maybe hundreds of thousands of other sites that have that some piece of information. Are LLMs supposed to link to every site?
Most sites do not have unique information at all, and even sites that do rarely contain only unique information. Implying that most sites deserve links because they were crawled seems like a statistical fallacy. You could say the same thing about the percent of sites crawled by Google versus ever showing up on first page of results.
The issue arises when we move away from objective trivia and into subjective, localized buying intent, which is where the web monetizes itself.
If I ask an LLM 'who created Linux?', there is one right answer. But if I ask an LLM 'who are the best commercial roofers in Houston?', there isn't one right answer. There are dozens of highly qualified local businesses that do possess unique value, unique pricing, and unique availability.
When an LLM answers that roofing question, it typically cites 3 to 5 businesses. The other 40 legitimate roofing companies in the area are left out. My study isn't arguing that every single one of those 40 companies deserves to be in the answer; it's pointing out that those 40 companies currently have no idea they are being left out.
In the Google era, if you weren't on page 1, you could look at Search Console, see your ranking, check your backlinks, and understand why. In the AI Search era, businesses are being scraped to build these answers, but they have zero telemetry on whether they are actually making the cut. This index is just an attempt to provide that missing telemetry.
I don't see how mentioning the other 40 should follow from that fact. You get exactly what you're asking for - the best, according to agent's judgement. Judgement and processing of simple search results is exactly what you're using the intelligent agent for. Ask for a list of all commercial roofers in Houston and then you can expect a list.
It can be argued that models are clustering their replies around a few options when asked to choose from a list of equally valid ones (due to mode collapse, distribution biases, primacy/recency biases etc), but these options are not equally valid for the agent, it looks at their sites or possibly in some other places like review sites to rank them. Its ranking criteria might be not good at all, but that's another question.
Also, I would assume that really "GEO" is just like "SEO". If you rank on the '1st page' of results for whatever common searches, you are _very_ likely to rank the same way for LLM questions, because all the LLM is doing (nearly all of the time for 'best roofers in Houston') is doing a web search and summarising the first x results. So if you are on page 1 for that term, it's very likely IME that you will get mentioned on LLM answers for that.
You're effectively reminding us that brands don't get understand well how to influence ai seo
Also what is the incentive for me to rank on LLMs? People do not use it to click sources or even take action. Do you have any proof that being ranked as the best roofer in houston drives sales more than not being ranked as it? If not, why should i care as the roofer?
I think your assessment of this result being as expected... but this is about the LLM equivalent of SEO becoming an area of interest
Not even Wikipedia.
This appears to be a new generation of "SEO", marking itself as a service for getting into AI results?
This is not the future I want to be a part of.
Perhaps it's inevitable that after a break from everything being driven by money that LLMs will now be ruined by people spending $X to get into AI to make back $X+1, leading to an arms race of ever increasing X, to the detriment of users.
1- LLM Crawler Optimization: the new SEO
2- Weightings-For-Pay: for a fee have your product or service come up more frequently in associated answers
No, it is saying that even the ones that are being scraped are not being included in results
You're absolutely right to be concerned about an arms race. If the data showed that doing X, Y, and Z guaranteed a citation, we'd be right back to the worst days of keyword stuffing.
But what this data actually shows is the opposite: even if you do everything 'right' (allow the retrieval bots, provide perfect machine-readable schema, don't block anything), you still have a 94.8% chance of never being cited. How LLMs cite is entirely opaque.
I built this to give site owners a baseline measurement of what is actually happening to their content today (as a free, anonymous view), so they can make an informed decision on whether keeping their doors open to these crawlers is actually worth it.
The title is very misleading - authors openly state the low and slewed sample pool.
This seems consistent with Sturgeon's Law.
If websites charge pennies per AI crawl, they will make more money than ever being reference in that 6.2% of websites that get cited (of which even another small percent get any follow through that leads to a sale or ad click)
HTTP 402 also basically extends the pay per token model people have gotten used to with AI model providers, except applied to the whole web, with the added privacy benefit in that there is no need for sellers of content to “know their customer”, and indeed it may even be impossible to do so because of how the payment gateways operate.
1. The market for lemons. In fact sites trying to "monetize their content" are the most likely to be lemons, so just asking is a signal that your "content" is not worth anything.
2. If your information does have value, it competes in a market with other sites full of high quality information that aren't trying to monetize it, meaning your specific information has to be specifically very valuable and not available yet for free elsewhere. If it is valuable as information (i.e. not something like creative writing), it will quickly spread and become freely available.
Lots of "content" just isn't worth anything (or has negative worth: it wastes your time). e.g. consider youtubers begging to get viewers to like/subscribe to increase their reach, and people generally don't despite it costing them nothing. Because it's not even worth a click to them.
Blocking LLM crawlers has become the number one use case for our IP database customers at https://focsec.com/
It feels lazy that this isn't built into the harness in some adversarial citation review checkbox.
Also the questions LLMs ask are so inhuman. It's like it's some contrarian trying to win an argument over getting an answer.
It is commonly this way right now, but it doesn't have to stay this way forever.
It is also common, in my usage at least, to iteratively brow-beat the bot into paring its statements down to those that which are supportable by its sources. Doing so just takes repetition, and that repetition takes time and burns more tokens.
With the present state of things, the prompts to get moving on this and to guide the ultimate response into something that is verifiably supportable by outside sources can usually be simple and largely generic.
They're easy enough prompts that a subagent can produce them.
(I've done it myself with Codex subagents and it worked very well, aside from the unsustainable burn rate that did not fit my budget.)
BUT
On models with web search, they can scan the sources in context, and those are pretty good at correct citations. However it's still a "trust, but verify" situation.
Think about your coding harness: the model reads a bunch of files into the context, and it generally doesn’t forget/hallucinate which lines came from which file.
I mean, its not. Lots of LLM's focused on finance do this already.
Bloomberg's own ASKB produces results and provides links back to the source documents or urls that it references so people who care about correctness can verify the results.
However, if you are looking for the best product/service/whatever, then yes it really does matter. I've bought _so_ many products because of LLM recommendations. For example, I wanted a new webcam, I asked the LLM to find me the best ones with a large sensor and Linux compatibility. It gave me a shortlist then I chose one and then I bought it.
This experience is far better than trailing through dozens of pages of (even pre LLM) SEO slop.I just tried the same on Google search and all the links recommended a camera with ~10% the sensor size that I bought.
I would expect a judgement of a modern model to be superficial for this request, but still better than nothing. A deep research agent might be a lot better and much less susceptible to "optimization".