Define "negligible".

You need to know how much LLM output you need to get your product working, before you even know what you're hoping for regarding a target cost per million tokens. When you do get PMF, can some of the work be offloaded to a smaller and cheaper model? Can you determine this division of labour yet?

Consider also that "computer" used to be a job title, that since then the cost of doing computations has reduced by a factor of at least 1e14, and yet that you're only asking this question at all because you're still compute limited.

If they do start to become unsustainable you might see more companies moving to a BYOK or usage-based billing model. If they do that, I don't know if the use cases for AI would justify the cost for consumers (but perhaps so for businesses). There's been a ton of build out of data centers so I do think the cost reduction we've seen so far may extrapolate but at the expense of more performant models. Hard to tell right now though

At some point AI providers will need to break down profit/token and price accordingly. Right now, they are losing money to gain market share. Also, AI consumers will need to get the expense of AI into their own profit calculations.

Hard to say how it will play out, aside from both sides are going to strive to maximize their own benefit, and time will tell how the actual numbers balance out.

This is one reason why it matters whether or not the AI bubble is all hype. There is a non-trivial chance that once people truly figure out the monetary value of AI's help on their processes and cut out all hype-based use cases... their spending limits to reach that value might not match what the providers need to run the platforms.