Data spies: The dark and shady practices of Silicon Valley | Roger McNamee | Big Think
After the internet bubble burst in 2000, there was a period where the venture capital industry retrenched, and a void was left. A group of people saw an opportunity. They're now known as the PayPal Mafia. This was Peter Thiel, Elon Musk, Reid Hoffman; they had two of the most brilliant insights in the history of entrepreneurship.
The first was that the internet was about to make a pivot from being a web of pages to a web of people. They called it web 2.0. This is what created the basis for all of social networking. The second insight was probably even more powerful than that. It had turned out that for 50 years before that time, Silicon Valley had basically struggled with the limits of technology. You never had enough processing power, memory, storage, or bandwidth to do what the customer wanted.
So every product had to address just a piece of the customer problem. The notion of making a global product, like Google or Facebook, never occurred to anyone because you never had enough resources to get it done. The PayPal Mafia realized that Moore's law and Metcalfe's law, the two laws that talk about processing power and networks, were about to hit crossing points, where there would be enough resources to do whatever you wanted to do. And it happened to coincide exactly with their insight about web 2.0.
They subscribed to a form of libertarianism that basically not only praised the individual, but it had this notion that you could disrupt; you could change things and not be responsible for the consequences of your actions, which was incredibly convenient if you're about to go out and create giant global enterprises. The notion that you could just knock things over and it was somebody else's problem, that allowed you to do pretty much anything.
This notion of blitzscaling, eliminating friction of all kinds so you can grow as rapidly as possible to global scale. And the problem with eliminating friction is you eliminate the ability of populations to adjust to change. Things happened so quickly, there's no opportunity for evolution. And these guys went, as you saw, in a decade from nothing to global. And in 15 years, they went from nothing to global domination.
But they started with this notion that the product had to be free. You had to get rid of the friction of a purchase price. So if you're going to have a free product, it's got to be supported by advertising. And if you want to have advertising be valuable, people had to pay attention. They got into this notion that they would manipulate attention, first with rewards to create habits. They would give you likes. They would give you notifications to get you to come back. And they would build habits that for many people turned into addictions.
But the other thing they did was when they got you there, they appealed to the lizard brain, the low-level flight-or-fight emotions that cause people to become tribal. They appeal to outrage and fear. Why? Because when you're afraid or when you are outraged, you share the source of that fear and outrage with other people. Because if they share that with you, that emotion, you're going to feel better. And that worked really, really well. It caused people to share a lot of stuff and see lots of ads.
If they'd stop there, we probably would have been OK. But they didn't. Google had a brilliant insight in 2003. Actually, they had it earlier. They patented in 2003, this notion of behavioral prediction. What Google discovered was that the data they captured from their users that they used to improve the search engine captured tons of data they had nothing to do with improving the search engine. But they discovered it told them a lot about what people were going to do. It gave them a way to predict behavior.
Traditionally, business collected data to improve the product or a service for the person from whom they collected the data. But behavioral prediction, as practiced first by Google, then by Facebook, and now also by Amazon and, I believe, by Microsoft, is really about taking data from one person and applying it to somebody else. So the person w...