People have been trying this for a really long time and there are just a ton of business considerations that make it impractical. 1. People buy photography, ev

People have been trying this for a really long time and there are just a ton of business considerations that make it impractical. 1. People buy photography, even stock photography for ads or business uses, with some intention to affiliate with the photographer / artist or some notion of an artsy style or aesthetic. Autogenerated art starts right off the bat at a disadvantage for being “commodity” in nature, even compared to repetitive inventory of sites like Shutterstock. Maybe you can get past this for certain niche areas where the real photos are already exceedingly commodity, like backgrounds, office photos, landscapes. But even then, the status of an artist counts for a lot. 2. It’s not actually that cheap to operate generative image at scale. You have to ensure that pre-generated content is of sufficient quality, and covers sufficient subject matter, compositional and aesthetic variety. If content is generated on the fly, you’ll be dealing with pretty high throughput on a very resource intensive model. 3. Competition can replicate your image model pretty easily, so your differentiator comes back to branding and a sense of “not commodity” quality, as well as all accompanying services and support, which is where all your operating costs come from anyway. I am sure generative inventory will become a bigger trend in stock photography, but I doubt it will be much of a differentiator. If you run a stock photography business with more than a few million images already, you would be better served building ML solutions for search, discovery, keyword or caption annotation, abusive content detection, automated aesthetic enhancements or assisted editing tools & style transfer. There won’t be a “holy grail” of generated inventory. Most customers just won’t care. It’s a good example of how impressive exotic ML solutions might seem like they surely have to have consumer applications, but where it interacts with business concerns it just doesn’t matter. Monetizing ML solutions is really hard - much harder than creating the ML solutions in the first place.