Maciej Cegłowski has made the case that we are in an Advertising Bubble, and that when investors realize they cannot realize the gains they expect there will be hell to play. We are reminded that a naive faith in complicated mathematical models has been an underlying cause of most modern bubbles, and explore the connection to big data here.
First, the problem. As Maciej Cegłowski notes, there is more money flowing into advertising than the products buying advertising can currently support. The difference is explained by investment, fueled by dreams of making returns impossible to make in conventional advertising.
If you look at the size of the lines here — roughed out but essentially correct in scale — it’s difficult to imagine that the amount of money flowing into product (our pink/salmon line) can support the amount of money flowing into what Ceglowski calls adtech (Google, Facebook, Instagram or any other tech that relies on an advertising model for revenue. See [w l=”Advertising Bubble”]
So why do investors think their money will garner return? The answer, roughly, is Big Data. With data collected and some rather intense math not fully understood by the investors it is believed the old rules of return no longer apply. (Ceglowski has talked about this before as well, but I cannot find the cite).
What’s interesting is how much this mirrors previous bubbles. Bubbles require, first and foremost, some piece of Voodoo Economics complex enough to fool investors, but resonant enough to draw them in.
Most recently, we brought the world economy down because we somehow believed that that we could pool not just some risk away by organizing subprime loans into tranches, but in fact eliminate risk entirely. That was made possible by the complexity of the process. [link l=”https://medium.com/@danwwang/the-cdo-the-cds-and-the-subprime-mortgage-crisis-c1aa28c01116″ t=”Medium explains the credit default swap.”]