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Comment by nneonneo | Hacker News Reader

You have no guarantee the API models won’t be tampered with to serve ads. I suspect ads (particularly on those models) will eventually be “native”: the models themselves will be subtly biased to promote advertisers’ interests, in a way that might be hard to distinguish from a genuinely helpful reply.

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> You have no guarantee the API models won’t be tampered with to serve ads. I suspect ads (particularly on those models) will eventually be “native”: the models themselves will be subtly biased to promote advertisers’ interests, in a way that might be hard to distinguish from a genuinely helpful reply.

I admit I don't see how that will happen. What are they gonna do? Maintain a model (LoRA, maybe) for every single advertiser?

When both Pepsi and Coke pay you to advertise, you advertise both. The minute one reduces ad-spend, you need to advertise that less.

This sort of thing is computationally fast currently - ad-space is auctioned off in milliseconds. How will they do introduce ads into the content returned by an LLM while satisfying the ad-spend of the advertiser?

Retraining models every time a advertiser wins a bid on a keyword is unwieldy. Most likey solution is training the model to emit tokens represent ontological entries that are used by the Ad platform so that "<SODA>" can be bid on by PepsiCo/Coca-Cola under food > beverage > chilled > carbonated. Auction cycles have to match ad campaign durations for quicker price discovery, and more competition among bidders

you mean the API response then will contain the Ad display code?

Or worse subtly integrate companies that pay them into the answers.

The generated text will contain advertisements.

Companies will pay OpenAI to prioritize more of their content during training. The weights for the product category will now be nudged more towards your product. Gartner Magic Quadrant for all businesses!

More akin to something like the twitter verified program where companies can bid for relevance in the training set to buy a greater weight so the model will be trained to prefer them. Would be especially applicable for software if azure and aws start bidding on whose platform it should recommend. Or something like when Convex just came out to compete with depth of supabase/firebase training in current model they could be offered to retrain the model giving a higher weight to their personally selected code bases given extra weight for a mere $Xb.

But this is upfront, during training?

How does X then change "on the fly" if ad deals are changing? Constantly re-training with whatever advertiser is the current highest paying on?

In google ad times, this was realtime bidding in the background - for AI ads this does not work, if Im right?

Companies pay for entire sports stadiums for brand recognition. That’s also not something you can change on the fly, it’s a huge upfront cost and takes a significant effort to change. That doesn’t stop it from happening it’s just a different ad model.

The llm output will just contain ads directly. It’s going to be super hard to tell them apart from normal output.

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