"Let's make sure our model fails sometimes so that we can bill more for a second agent to validate, sometimes correctly, the work of the first model."

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Aurornis 3 hours ago | parent [-]

If you’re implying that the LLM companies are trying to train their models to make malicious tool calls so they can collect a few more tokens on the review, then I don’t know what to say. I guess threads like this are just a breeding ground for conspiracies now?

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wang_li 2 hours ago | parent [-]

I think that solving a problem they have that has a side effect of generating more revenue is not lost on them. Particularly in the situation where the cost to improve the models goes vertical for minimal improvement in an asymptotic fashion. The decision to spend 6-7 figures regularly on training or bill every customer 3-4 figures per month isn't hard. While I don't think they will explicitly impair the models, they will certainly make choices that externalize the shortcomings of the model to their customers.

	▲	Aurornis 2 hours ago \| parent [-]
		> I think that solving a problem they have that has a side effect of generating more revenue is not lost on them. I think you’re overestimating the revenue generated by this. Having a separate LLM with a cached input prompt check commands is a trivial adder. The only reason it comes up is because they explain to users that it comes out of their plan. So someone on a $20/month plan is going to hit their limits marginally, though mostly negligibly, faster. If you think they’re sitting in a conference room scheming about making their main models worse on purpose to collect a few extra cents, that’s just baseless conspiracy. They have more to gain or lose based on main model performance.