Dan Pink speaks with Brett McKay about his new book, When: The Scientific Secrets of Perfect Timing (podcast) [H/T Abnormal Returns] (LINK)
Exponent Podcast: Episode 136 — It’s All 1s and 0s (LINK)
a16z Podcast: Revisiting the Gene (LINK)
Robert Sapolsky speaks to CBC Radio Quirks & Quarks (LINK)
Related book: Behave: The Biology of Humans at Our Best and WorstBrain Cells Share Information Using a Gene that Came From Viruses - by Ed Yong (LINK)
Animals Have Culture, Too - by Ed Yong (video) (LINK)
The mysterious cycles of ice ages - by Matt Ridley (LINK)
Some great thoughts on network effects from Anu Hariharan on Twitter:
- Often misunderstood - Network Effects is not the same as scale
- One simple way to test for that is ask this question - what is the “barrier to exit” for the user?
- If the barrier to exit for the user is low, then there is no network effect. This implies it is easy for users to switch from your service
- Ride sharing services (Uber, Lyft) don’t have a network effect (in other words demand side economies of scale). Users often switch apps if it takes longer than 5 mins ETA or if there is surge pricing on one
- However ride sharing does have supply side economies of scale and therefore opportunity for select players to have monopolistic share in a market
- On the other hand apps like Facebook, LinkedIn have very strong network effect - because the barrier to exit for the user is really high!
- A user has invested time and effort in building a social graph on these platforms with connections, history of exchanges and in some cases even maintain them. It is not easy for customers/ users to switch easily and therefore the “barrier to exit” for the user is really high