Disregarding the article, I sort of disagree with your comment too, maybe you are oversimplifying it too much. Or maybe because it's kind of a chicken and the

Disregarding the article, I sort of disagree with your comment too, maybe you are oversimplifying it too much. Or maybe because it's kind of a chicken and the egg kind of problem - which came first, the algorithm or the data. Sure, generic sort algorithms come "off-the-shelf", but that is not true for all algorithms. Not all algorithms are public knowledge if they are patented, proprietary, or restricted in use/distribution. You really need both - algorithms and data, right? And systems of execution for algorithms... and networks to interconnect the systems... and potentially humans to review the algorithm's output... and..... and... and.. I think the key is, algorithms can also represent embedded capabilities (information? knowledge? insight? not sure what to call it) generated from data. I mean, look at a RNN - the values/weights/etc. calculated inside the model are calculated from observations/training on the data. Once you disconnect the algorithm from the data itself, the algorithm alone now has embedded value without the data, potentially enormous. Or, expert/rule-based systems which contain algorithms explicitly designed to embed human knowledge - those algorithms may not even be trained on actual data, they are simply a simplified expression of codified human knowledge. If you go one step further and try to make some assumption that the way our brains work is by codifying inputs we receive into algorithmic models... and then using those algorithms to calculate based on new inputs from the environment... we can't keep all the data in our heads, but we can probably keep more algorithms than data. Maybe I'm being too philosophical, but in my opinion, it's not just about the data.