But no, they want to pay $0.1 per request to recognize if a photo has a person in it by asking a multimodal LLM deployed across 8x GPUs, for some reason, instead of just spending some hours with CLIP and run it effectively even on CPU.
This is the bottleneck in my experience. Going for the expensive per-request LLM gets something shipped now that you can wow the execs with. Setting up a whole process to gather and annotate data, train models, run evals, and iterate takes time. The execs who hired those expensive AI engineers want their results right now, not after a process of hiring more people to collect and annotate the data.
I think you've just identified, in a set-theoretic complementary manner, the TAM for GenAI.
There have been some developments in the image-of-text/other-than-photograph area though recently. From Meta (although they seem unsure of what exactly their AI division is called): https://arxiv.org/abs/2510.05014 and Qihoo360: https://arxiv.org/abs/2510.27350 for instance.