Disagree. Anybody running appreciable ML workloads buys and racks their own GPUs.
Moreover, say, a tier 2 player like a major AdNet rents its CPU time at rates that make AWS users cry. For them, it makes even more sense to just "let the code be a horrid contraption written by 10$ per hour outsourcing shop, but we can run in on cheap hardware"
* Uptime guarantees - AWS goes down. Running your own hardware locally, you at least have a recourse.
* Storage and bandwidth - some of these datasets are huge and not amenable to streaming onto a temporary rental.
* Security - the ever-present bogeyman of cloud services. It might be harder to lock down your own machines, but it's the only way to fulfill some contractual requirements.
For commodity processing that doesn't involve these specialties, sure, fire away. That's what it's good at, and what most people need most of the time. But there's always a final TCO number and arriving at it requires accounting for all the risks.