So when you have a bad take machine, you get the following processes:
They make a bad take.
People are outraged and talk about it.
The bad take machine likes it and does more of that behaviour in future.
If, on the other hand, they make a take and nobody cares, they do not get reward and the behaviour is selected against.
The behaviours drove the spread of the outrage replicator, and the outrage replicator provides the selection mechanism for the behaviours. Thus, via the spread of our outrage on Twitter, we have operant conditioned the bad take machine into producing worse takes.
Which is to say, it's bad on purpose to make you replicate it.
Whilst Feature Parity often sounds like a reasonable proposition, we have learnt the hard way that people greatly underestimate the effort required, and thus misjudge the choice between this and the other alternatives. For example even just defining the 'as is' scope can be a huge effort, especially for legacy systems that have become core to the business.
Most legacy systems have 'bloated' over time, with many features unused by users (50% according to a 2014 Standish Group report) as new features have been added without the old ones being removed. Workarounds for past bugs and limitations have become 'must have' requirements for current business processes, with the way users work defined as much by the limitations of legacy as anything else. Rebuilding these features is not only waste it also represents a missed opportunity to build what is actually needed today. These systems were often defined 10 or 20 years ago within the constraints of previous generations of technology, it very rarely makes sense to replicate them 'as is'.