4AAVC101 - week 7
« Back to 4AAVC101These are my notes from November 07 for 4AAVC101 at King's College London for the 2017-2018 school year. The lecturer, Nick Srnicek, is the author of two excellent books at the intersection of technology and leftist politics: Inventing the Future (with Alex Williams), and Platform Capitalism.
The usual disclaimer: all notes are my personal impressions and do not necessarily reflect the view of the lecturer.
Advertising and the surveillance economy
Readings
Big other by Shoshana Zuboff
A paper on surveillance capitalism. Challenges the idea of technological determinism when it comes to big data (it’s shaped by the contours of capitalism etc etc). Focuses on Google’s data extraction practices, as understood via publications written by Google’s Chief Economist Hal Varian. Also distinguishes between the economics of high tech corporations (high rev/employee ratio) and the giants in older industries (e.g., Detroit automakers). References Arendt. The “Big Other” as the catch-all term for a totalitarian, computer-mediated surveillance system from which we cannot escape; the biggest consequence is a shift from the means of production as the determinant of power to the means of behavioural modification. The upshot of this kind of technology embodied in a corporation is the undermining of “the historical relationship between markets and democracies”. Pretty great & well worth reading.
The Secrets of Surveillance Capitalism by Shoshana Zuboff
Similar to the paper above but written for more of a popular audience. Says that demanding privacy from surveillance capitalists constitutes an existential threat, and that we are ceding our sovereignty and capability for individual self-determination to corporations like Google (which capture “behavioral surplus”). Also quite great.
Lecture
- in 2016, almost $80 billion was poured into digital advertising, a 20% increase over 2015
- much of that goes into Facebook and Google, which constitute a duopoly in this field
- the historical context of their case comes from the dotcoom boom
- most of the companies that rose to prominence during that era had no revenue plan (basically Ponzi schemes preying on the hopes & dreams of investors)
- in the ensuring collapse, most of these companies died, which had a winnowing effect (chaos is a ladder etc)
- the ones that remained, or were founded in the wake of the catastrophe, had to be smarter about revenue models
- especially since VC funding dried up post-crash (with a few exceptions)
- but it also meant that the field had been sufficiently cleared that the rest were well-poised to become monopolies
- on how ad auctions work: both the cost of the bid & relevance are taken into account (as well as other factors)
- Srnicek says that data isn’t actually being “sold”, so it’s not a commodity (i.e., it never leaves the confines of FB’s ecosystem)
- it’s just being used to match you with the optimal advertisements
- FB’s desktop revenue is dropping (as a share of total revenue) and mobile is taking over
- challenges of mobile ads
- you’re less likely to click on them (as it means switching out of the current app context) * small screen means they take up more of the screen -> anying
- cross-device tracking (though device ID allows that to be done deterministically)
- possibility of accidental clicks (which FB counters by saying that the advertiser is still building brand awareness)
- my thoughts: on the other hand, it’s harder to block via adblock (mobile apps are usually more locked down)
- challenges of mobile ads
- on surveillance
- some defining characteristics
- focused on individuals, not just groups
- systematic and deliberate
- routine and repeated
- can be based on either private or less obviously private data (mostly metadata—keystrokes, likes, etc—which can still be useful for predictions), or both
- some defining characteristics
- thus FB/Google are the ones who have power in this new advertising-based economy
- power over users, in the psychological sense (they convince us to buy crap we don’t need)
- but also power over businesses, as their algorithms can make or break corporations
- they can also pass on this data to governments/employers
- and, of course, they can affect politics
- Zeynep Tufekci says a lot of valid stuff on this