Are consensus (and atomic broadcast) protocols limited by single node's resources? The main question is the following:
When designing a system supporting atomic broadcast, can it be proved theoretically that the performance &amp; scalability dimensions (i.e. latency, throughput, dataset size) of the system are limited by the performance of a single node?

To give an example:
A system based on partitioned-logs, like Apache Kafka, can provide ordering guarantees in a single partition, but it can't provide any ordering guarantees between different partitions. However, this gives Kafka the capability to scale to extremely large datasets. I was contemplating whether it would be possible to create a system that could provide the ordering guarantee for the whole dataset, while also allowing the dataset's size to increase in the same quasi-linear way.

My speculative answer to this is no for the following reason:
It's been proved that the atomic broadcast problem is equivalent to the consensus problem [1]. Based on the fact that the consensus problem requires an elected leader, which drives the consensus process, I concluded that the scaling capabilities of such a system is limited by the resources of a single node.

Are there any flaws in my thinking ?  

[1]: https://en.wikipedia.org/wiki/Atomic_broadcast#Equivalent_to_Consensus
Your argument for "no" is flawed, for two reasons:


When we say that X reduces to Y, we mean that a solution to Y is one way to solve X.  But there might be other ways to solve X that don't rely on solving Y.
Some protocols for consensus elect a leader, but that doesn't necessarily imply that all approaches to solve consensus require electing a leader.  (For example: consider Bitcoin.  No leader, but it arguably solves a consensus kind of problem.)