Not to mention, I can imagine it's frustrating uploading many gigs of data to amazon.
Assuming 2.5MB/s Upload capacity with local data, you've uploaded it to your deep learning machine in half a day (100000 / 2.5 / 3600 ~ 11 hours) - which is not that much, as most of your time will be used for development and fine-tuning of your deep learning tool chain anyway. In most cases the data is accessible via a public-facing service, and assuming 1GBit/s bandwidth you've downloaded 100G in 13 minutes (100000 / 125 / 60 ~ 13 minutes).
I know egress is one of the more expensive cloud services (e.g. compared to compute and storgage) at AWS, GCP, etc., but if I upload data to my learning system that's ingress AFAIK which is mostly free or less expensive. Btw. current Egress is like 0.1$/GB, so 100G ~ 10$.
Don't get me wrong, I am not saying you should always train in the cloud, but I do not think slower Upload or Ingress are the limiting factor.
The anti-competitiveness is getting worse too. Charter just took over TWC in my area.