Yes, and OpenAI has enough financial resources to do a bespoke abstraction layer with multiple provider-specific performant implementations.
Regardless of whether they bring in the Kubernetes complexity.
(Internal codename: Goober Yetis.)
Why would they create a bespoke abstraction layer instead of just relying on k8s?
There is only pain on the path of recreating it, it will end up almost as complex as k8s and it will be hell to hire and train for. Best to just use something battle-tested that works with a large pool of people trained for it, even better: their own LLM has gobbled up all the content possible about k8s to help their engineers. K8s complexity came to be for reasons discovered during growing the stack which anyone doing a bespoke similar system might run into, and it's pretty modular since you can pick-and-choose the parts you actually need for your cluster.
Wasting manpower to recreate a bespoke Kubernetes doesn't sound great for a company burning billions per quarter, it's just more waste.
I trust that OpenAI engineers are smart enough to build a replacement Kubernetes. I also think they are smart enough to build better, more innovative desks, tables & chair. But both of those wont be a good use of their time.
There's a reason why company vision & mission exists. OpenAI's mission is not to build next k8s, but to build better AI models.