This ability for consumers to tap into specific voices will also be critical as more content and more users come online. Consumer time (or “attention”) doesn’t scale with either the volume or ready availability of content at their disposal. Discovery functions, too, have a maximum. The significance of 1,000 likes, 400 ratings or 3.2M plays is very different with 3B Internet users than it was with 500M. Not only does contextualizing these social cues become impossible, but the demographics of the reviewers continues to change – first in terms of age and income, then geography and culture – making it difficult to understand the personal validity of any crowd based metric. That’s not to say that a product on Amazon with 1,400 reviews and a 3.8 star rating isn’t good – just that the common review mechanisms found across the web mathematically soften taste out to the average. This works a lot of the time, but we tend to have very particular tastes in certain categories – and there is a certain staleness created by narrowing these averages down using look-a-like groups and other algorithmic techniques. Not to mention the fact, that such an approach often lacks the element of serendipity and surprise from discovering something you loved but didn’t expect (especially if you would otherwise have avoided it). As a result, curators both solve a media painpoint and enrich consumption.