Comment by andy99
11 hours ago
There would be competition from API wrappers, if you want to pay there will always be lots of options to chat without ads. I hate to think what they and others might come up with to try and thwart this.
11 hours ago
There would be competition from API wrappers, if you want to pay there will always be lots of options to chat without ads. I hate to think what they and others might come up with to try and thwart this.
I think ads will take the form of insidious but convincing product placement invisibly woven into model outputs. This will both prevent any blocking of ad content, and also be much more effective: after all, we allude to companies and products all the time in regular human conversation, and the best form of marketing is organic word-of-mouth.
I just saw a sibling post about Kagi, maybe this is how the industry will end up, with a main provider like OpenAI and niche wrappers on top (I know Kagi is not just a google wrapper but at least they used to return google search results that they paid for).
I thought you were going to say “that comment recommending Kagi is exactly what those ads would look like: native responses making product recommendations as if they’re natural responses in the conversation”
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I don’t know how subtle or stealth you can be in text. In movies, there’s a lot of stuff going on, I may not particularly notice, I’m going to notice “Susie, while at home drinking her delicious ice cold coca-cola….”
> I’m going to notice “Susie, while at home drinking her delicious ice cold coca-cola….”
It will be much more subtle. Asking an LLM to help you sift through reviews before you spend $250 on some appliance or what good options are for hotels on your next trip…
Basically the same queries people throw into google but then have to manually open a bunch of tabs and do their own comparison except now the llm isn’t doing a neutral evaluation, it’s going to always suggest one particular hotel despite it not being best for your query.
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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.
> 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?
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.
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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!
The llm output will just contain ads directly. It’s going to be super hard to tell them apart from normal output.
Or worse subtly integrate companies that pay them into the answers.
The generated text will contain advertisements.