The same happened to image recognition. We have great algorithms for many years now. You can't make a company out of having the best image recognition algorithm, but you absolutely can make a company out of a device that spots defects in the paintjob in a car factory, or that spots concrete cracks in the tunnel segments used by a tunnel boring machine, or by building a wildlife camera that counts wildlife and exports that to a central website. All of them just fine-tune existing algorithms, but the value delivered is vastly different.
Or you can continue selling shovels. Still lots of expensive labeling services out there, to stay in the image-recognition parallel
The key thing is AI models are services not products. The real world changes, so you have to change your model. Same goes for new training data (examples, yes/no labels, feedback from production use), updating biases (compliance, changing societal mores). And running models in a highly-available way is also expertise. Not every company wants to be in the ML-ops business.
Or you can continue selling shovels. Still lots of expensive labeling services out there, to stay in the image-recognition parallel