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This is where LLM advertising will inevitably end up: completely invisible. It's the ultimate "influencer".

Or not even advertising, just conflict of interest. A canary for this would be whether Gemini skews toward building stuff on GCP.

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Considering how little data needed to poison llm https://www.anthropic.com/research/small-samples-poison , this is a way to replace SEO by llm product placement:

1. create several hundreds github repos with projects that use your product ( may be clones or AI generated )

2. create website with similar instructions, connect to hundred domains

3. generate reddit, facebook, X posts, wikipedia pages with the same information

Wait half a year ? until scrappers collect it and use to train new models

Profit...


It is a valid concern. We are firmly in the goldilocks phase of LLMs, like in the first couple of years of Google when it was truly amazing. Then SEO made Google defensive, then websites catered to Google and not users, then Google catered to Google and not websites and we end up with 30 page recipe sites.

LLMs are obviously different and will have different challenges, but their advantage is how deep into a user's request they go. Advertising comes down to a binary choice - use product X or not. If I want implementation instructions for a certain product on specific hardware an ad will be obviously out of place and irrelevant.

So "shopping comparison" asks might get broken, but those have been broken for a while.


There wouldn't be an "ad" anywhere, though. You'll just ask the LLM for alternative implementations in plan mode, and it will be selling you one of them during the conversation rather than giving you an unbiased comparison. If you become suspicious it will make sure the pros just slightly outweigh the cons, or mention how well the thing works with something else in your stack, or whatever else a skilled salesperson would do to guide your choice without you realizing.

It's already doing this by telling everyone to use React and Tailwind, it's just that nobody's getting paid for it to do that.


> Then SEO made Google defensive, then websites catered to Google and not users,

Google was created in response to simple proto-SEO techniques (e.g. keyword stuffing) that already ruined Alta Vista.

Google has been combating adversarial information retrieval since inception.

Google's background with that is one of the reasons to expect they will stay on top of the AI race. The recipe is: lots of good/novel data x careful weighting of trust x algorithm.


from my understanding Anthropic are now hiring a lot of experts in different who are writing content used to post-train models to make these decisions and they're constantly adjusted by the anthropic team themselves

this is why the stacks in the report and what cc suggests closely match latest developer "consensus"

your suggestion would degrade user experience and be noticed very quickly


I guess that’s why I’m not seeing anyone trying to build a skills marketplace for agent skills files. The llm api will read in any skills you want to add to context in plain text, and then use your content to help populate their own skills files.

So I wonder about sharable skills? Like if it's a problem that lots of people have, I find the base model knows about it already.

But how to do things in your environment? The conventions your team follow? Super useful but not very shareable.

Whats left over between those extremes does not seem to be big enough to build an ecosystem around.

Final problem, it seems difficult to monetise what is effectively a repo of llm generated text files.



That sounds too expensive to be viable when the giveaway phase ends.

That's how Google search worked back when it was at its most useful. They had a large "editorial team" that manually tweaked page ranks on a site-by-site basis.

The core graph reputation based page ranking algorithm lasted for a hot second before people started gaming it. No idea what they do these days.


Yeah but you can farm that out very cheap, and I don’t think they were even manually reviewing more than a small fraction of sites.

If you’re hiring experts to manually rank programming libraries, that’s a much more expensive position.


This is the major point the anti-scraping crowd misses.

If you want your ideas to be appreciated, you should do everything in your power to put those ideas into the brains of LLMs. Like it or not, LLMs is how people interact with the world now.


https://www.bbc.com/future/article/20260218-i-hacked-chatgpt... says it took way less than half a year to 'pollute' a LLM

that's very different and was more akin to prompt injection or engineering, depending on your perspective, with a very specific query to make it happen (required a web fetch).

Richard Thaler must be proud. This is the ultimate implementation of "Nudge"

Influencer seems like an insufficient word? Like, in the glorious agentic future where the coding agents are making their own decisions about what to build and how, you don't even have to persuade a human at all. They never see the options or even know what they are building on. The supply chain is just whatever the LLMs decide it is.

In my last conversation with a Google support person, I was sent a clearly LLM-generated recommendation to switch to a competitor's product. Either they're not doing this, or the support person wasn't using Gemini.

It's standard practice for customer support people to chase away unprofitable customers (in the US; no idea how Google works). Human or LLM, they may simply not want your business.

Probably closer to the Walmart / Amazon model where it's the arbiter of shelf space, and proceed to create their own alternatives (Great Value, Amazon Brand) once they see what features people want from their various SaaS.

An obvious one will be tax software.


how is it a conflict of interest for a google product to have a bias towards using google products?

As users we must hold some accountability. AI is aiming to substitute for humans in the workforce, and humans would get fired for recommending competitor products for use-cases their own company is targeting.

If we want a tool that is focused on the best interest of the public users, then it needs to be owned by the public.


"Conflict of interest" isn't exactly the right term. "Conflict of value proposition" perhaps? E.g., you're using Google search based on the proposition it will effectively find things for you, but that turns out to be not what it actually does.

> A canary for this would be whether Gemini skews toward building stuff on GCP

Sure it doesn't prefer THE Borg?


I wonder if aggregators will emerge (something like Ground News does for news sources)

LLM pattern [0] will probably eventually emerge as the best way to fight those biases. This way everyone benefits from token burn!

[0](https://github.com/karpathy/llm-council)


Advertisers will only pay if AI providers will provide them data on the equivalent of “ad impressions”. And unlabeled/non-evident advertisements are illegal in many (most?) countries.

It doesn't necessarily have to be advertisers paying AI providers. It could be advertisers working to ensure they get recommended by the latest models. The next form of SEO.

That's called LLM SEO now I believe.

There are competing terms currently being decided on by the market at large: AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization)

Candidly I am working on a startup in this space myself, though we are taking a different angle than most incumbents.

While it's still early days for the space, I sense a lot of the original entrants who focus on, essentially, 'generate more content ideally with our paid tools' will run in to challenges as the general population has a pretty negative perception of 'AI Slop.' Doubly so when making purchasing decisions, hence the rise of influencers and popularity of reviews (though those are also in danger of sloppification).

There's an inevitable GIGO scenario if left unchecked IMO.


> I am working on a startup in this space myself

Do you see it as a positive contribution or just riding the gold rush?


Positive contribution to his Net Worth. Why would anything else matter?

> There are competing terms currently being decided on by the market at large: AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization)

It really annoys me the industry seems to be narrowing in on the two worse options rather than AIO.


I'm curious if there's any hard data on how LLM SEO compares to traditional SEO.

My gut tells me that LLM SEO will be harder to game than traditional SEO.


We shall see. The game might be harder, but the tools are better now too.

> data on the equivalent of “ad impressions”.

1. They can skip impressions and go right to collect affiliate fees. 2. Yes, the ad has to be labeled or disclosed... but if some agent does it and no one sees it, is it really an ad.

So much to work out.


How would it be paid for?

Affiliate fees.

Depending on an analysis just like in the post.

Maybe. Historically lots of ads had little to no stats and those ads were wildly more effective than anything we have today.

The AI provider still has to prove that they actually deployed the ad.

Advertisers pay for ads that don’t have impression data all the time. You can’t count how many people looked at a billboard or listened to your radio ad or paid attention to your televised ad.



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