Investor funds have been subsidizing the inference costs so far.
Investors might move from funding the model providers to funding the enterprises that use those models. That is, they might move from funding the cost of the experiment to funding the value of the result. No funding if there are no demonstrable AI gains.
This is a reasonable shift if this happens. If enough gains have been demonstrated, then investors might go back to funding the model providers. Investors always move towards the highest leverage point.
Strictly speaking, I don't think it is the generation or creation that diminishes their value. it is the consumption.
You said it too:
> If I see a million fake Tom Cruise videos, then it oversaturates my desire for desire for all Tom Cruise movies.
The trick of course is to keep yourself from seeing that content.
The other nuance is that as long as real performance remains unique, which so far it is, we can appreciate more what flesh and blood brings to the table. For example, I can appreciate the reality of the people in a picture or a video that is captured by a regular camera; it's AI version lacks that spunk (for now).
Note that iPhone in its default settings is already altering the reality, so AI generation is far right on that slippery axis.
Perhaps, AI and VR would be the reason why our real hangouts would be more appreciated even if they become rare events in the future.
I think we can simplify the answer to this question for most audience and say "the air is blue".
If they say, the air appears to be clear when I stare at something other than sky, the answer is you need more of air to be able to see its blue-ness, in much the same way that a small amount of murky water in your palm appears clear, but a lot of it does not.
If they ask, why don't I see that blue-ness at dawn or dusk, the answer is that the light source is at a different angle. The color of most objects changes when the light source is at a flat angle. And sun lights hits at a flat angle at dawn and dusk.
If they ask, what exactly is the inside phenomenon to see the sky color to be blue, then explanations like this blog are relevant.
If they ask, what exactly is a color, the answer is that it is a fiction made up by our brain.
The other is knowing what the collaboration brings to the table and shaping the rules of engagement to fit that expectation. Sometimes you collaborate with SMEs; they bring the domain knowledge - you don't, but you understand the goal better than them. Sometimes you are creating or refining the corporate strategy based on the actions from individual projects or partners; you are learning ground realities from them. Sometimes you need help from others to improve your take on a subject.
In each of these cases, you have to be clear about what you expect from the collaborators (and motivate them to contribute). Without being clear on what the collaboration is about and what they get in return is the number one killer of collaborative projects even though there is no ill-intent anywhere.
It boils down to whether your LLMs can speak graph queries better than SQL, for your use cases and data. As your data posture changes and your use cases change, you routinely reevaluate which DB query language suits best for LLMs.
I'd also design the system architecture in such a way that your non-agentic workloads don't suffer if you have to move between query models for serving agentic workloads better.
Investors might move from funding the model providers to funding the enterprises that use those models. That is, they might move from funding the cost of the experiment to funding the value of the result. No funding if there are no demonstrable AI gains.
This is a reasonable shift if this happens. If enough gains have been demonstrated, then investors might go back to funding the model providers. Investors always move towards the highest leverage point.
As long as AI delivers, this would be the rhythm.