It's actually not that much. Current consumer desktop hard drives top out at 8T (you can get enterprise RAID boxes of up to 48T now, but leave them out of the equation). 1P/day ~= 128 hard drives, so at 4min/drive, that's one person.

Of course, that's just YouTube, and Google has many other needs for data. But people forget just how big the denominators are on this quantity, and how effective Kryder's Law has been. They also forget how much reserve capacity there is in human labor; Google's datacenters have tiny employee counts because they are so automated, and could easily scale up into the exabyte/day range.

A more interesting question is what the differential rates of Kryder's Law vs. Moore's Law will do to how we architect software. Already, people in the know say that "disk is the new tape" - disk drive capacity has been increasing much faster than seek times, bus bandwidth, and available processing power, which means that you have to start treating the drive as a sequential storage device and not as a random-access platter. That's behind a lot of the shift from B-trees (as in conventional RDBMSes) to LSM-trees (as in BigTable/LevelDB), and also the resurgence of batch-processing frameworks like MapReduce. How does the software you build change when reading & writing data sequentially is really cheap, but accessing it randomly is expensive?