That criticism is specific to S3, not EC2 or Hadoop. It's perfectly feasible and probably preferable to have Hadoop work on local files in instance store volumes (or EBS if you're mad).
There is other issue with running hadoop on EC2 (w/o S3). Instance storage is relatively small - about 3.6 TB on largest instance and 1.5 TB on other "large" instances. In typical Hadoop machine I would expect about 8TB. So local storage is prohibitively expensive for the big data tasks.
In the same time - if we use local storage we a loosing elasticity - we have to run cluster all the time, even there is no jobs to run. It kills main point of using hadoop in the cloud - to pay for the computational resources on demand.
but instance store is transient! You may argue if you do triple replicated in different availability zones then you are ok. Well, in this case it would be very costly as you will end up with constantly spinning EC2 cluster. Even if you don't do any computation you must keep it all spinning. And see what happened to elasticity... you end up paying inflated cloud prices for constantly spinning fixed size EC2 cluster! Instead of being able to rapidly roll out large cluster, make the computation and fold it back and pay only for what you have used - isn't it the true promise of cloud?