You can be yourself, but being yourself may not get you what you want, and you’re probably wrong about who you think you are anyway. So goes the gist of Davidowitz’s Everybody lies. Does the truth even matter? The short answer is, yes: while we can choose to believe whatever we want, the value of recognizing the truth can be enormous.
Take this excerpt.
The outsize value of ignoring what people tell you
What people say: They don’t want to stalk their friends.
Reality: There is little in this world they want more than to keep up with and judge their friends.
Ipso facto: Mark Zuckerberg is worth billions.
What people say: They don’t want to buy products that are produced in sweatshops.
Reality: They will buy nice, “reasonably priced” products.
Ipso facto: Phil Knight is worth billions.
What people say: They want politicians to outline their policy positions.
Reality: They want politicians to spare them the details but seem tough and self-assured.
Ipso facto: Donald Trump
The emergence of big data is a breakthrough for the social sciences, adding validity to its practices. Data can effectively validate or invalidate assumptions, and also serve as a channel for discovery. Where social science went wrong in years prior is attempting to replicate the rigidity of hard science, a field where simple, distillable laws rule. But human nature is neither simple nor distillable. In the future, we’ll need nothing more (and nothing less) than curiosity, creativity and data.
Big data is our first real shot at understanding human nature. Beyond who we think we are and who we want to be, there is who we actually are: the sum of all the little actions we undertake day in and day out, some of which we’re aware of and others we’re not, while others still we lie about, both to ourselves and to others. I’ve always liked the adage that if you want to know who you really are, just look at how you spent your time that day; look at the line items on your bank statement — that’s where you’ll see your priorities. That’s where you’ll see what’s important to you — not in what you say is important to you. I know I haven’t always been happy when reflecting on this and examining the delta between who I think I am (schedule, budget, Netflix queue) and how my time and money are actually spent (what I did that day, bank statements, Netflix recommendations).
“Everybody lies…. People lie to friends. They lie to bosses. They lie to kids. They lie to parents. They lie to doctors. They lie to husbands. They lie to wives. They lie to themselves. And they damn sure lie to surveys.”
So where is the truth? Davidowitz says it’s with Google. Our private Google searches are revealing of our actual thoughts, worries and desires, and that’s what Davidowitz spends his book unpacking. While I don’t buy that we are our Google searches, as I think Google gets an outsized amount of the thoughts we deem too unsavoury for “more human” company, I do think the insight is undeniable. You shouldn’t compare your insides to someone else’s outsides, and you shouldn’t compare your Google searches to someone else’s social media presence.
One drawback of the data revolution is that the things we can measure aren’t always the things that matter, but they end up being the things we optimize. Enter the rise of addictive technology. What gets me is the story about video games, casinos and apps being A/B tested to optimize for maximum time spent and addiction. This data-focused explanation drove this idea home for me more than Nir Eyal or Golden Krishna did in their respective books. The odds are ever not in our favour as consumers.
A few more ideas:
Statistically, growing up near big ideas is more important than growing up with a big backyard
When it comes to data, it doesn’t matter if the algorithm makes logical sense, it only matters if it works (kind of a scary idea feeding into the black box AI fears)
To battle hate, don’t lecture. Instead give new information that may shed a different light and pique curiosity
The problems users actually have with your product or system or business may not be what they tell you; examine the data on how they actually behave to learn where they really struggle