Data acquisition is definitely hard, but it's far from the only challenge. Labeling is also hard for many use cases. Curating your labels is pretty annoying. Ma

Data acquisition is definitely hard, but it's far from the only challenge. Labeling is also hard for many use cases. Curating your labels is pretty annoying. Making your model inference performant enough to be launchable is also hard. Making your model something you can quickly iterate on is also hard. Evaluating your model (with the system it's embedding in) is also hard. I wouldn't say it has "nothing" to do with the models. Maybe "little to do with model architecture" and "a lot to do with everything around the model." There's just a lot of work to be done to get the business wins you want from ML.