GET requests? Probably not. PUT/DELETE requests? I think so, yes. All updates to the bucket ultimately bottleneck at an update to the meta-version of the bucket-record-object in the object-storage-system's bucket-metadata store (itself probably something like DynamoDB / BigTable / etc.)
Given the way these IaaSs' distributed KV stores all manage writes (i.e. by having a cluster of transactor nodes that per-key write-linearization responsibility for parts of the keyspace is sharded across — such that writes are fanned out to a particular designated transactor-node given the key's hash-slot), a very large S3 user, generating an extremely high level of metadata-update concurrency against a bucket, could very likely write-contend that bucket's metadata / have a "hot" bucket-metadata key; experience low perf due to that; and solve that by sharding the bucket (swapping one too-hot metadata key for N somewhat-hot metadata keys.)
I want to give an intuition-building example here, of an IaaS feature that wouldn't exist / wouldn't be exposed to the user if not for object-storage buckets being metadata-write-contended at scale. I'm not very familiar with the AWS ecosystem, though, so I'm not sure what the good example is for AWS. What I do know is GCP, so here's a GCP example: Google Cloud Dataflow allows you to set a temporary workspace GCS bucket on a per-job basis (gcsTempLocation). And, IIRC, Google's Cloud Architects advise to not have a bunch of active Dataflow jobs sharing the same gcsTempLocation — regardless of whether they use distinct key prefixes to namespace the temp files. Given that each job would be doing a lot of little serial updates to the temp bucket — and given that Dataflow jobs can each be highly internally concurrent — you're already potentially putting out O(N^2) ~concurrent updates to that bucket. You really don't want to make it O(N^3).