What do you think about the tension between inference accuracy and the types of edge applications used today?

For instance, if you wanted to train a multimodal transformer to do inference on CCTV footage I think that this will have a big advantage over Jetson. And I think there are a lot of potentially novel use cases for a technology like that (eg. if I'm looking for a suspect wearing a red hoodie, I'm not training a new classifier to identify all possible candidates)

But for sectors like automotive and defense, is the accuracy loss from quantization tolerable? If you're investing so much money in putting together a model, even considering procuring custom hardware and software, is the loss in precision worth it?