Cognitive Biases In Identifying Tech Disruption

We will admit to having a robust love/hate relationship with behavioral finance. In the plus column, it does highlight many useful themes largely untouched by classical finance. On the downside, its focus on human frailty can be disheartening. Life and investing are challenging enough without the constant reminder that your greatest enemy is often the man or woman in the mirror.

Shift the conversation to how cognitive biases shape investment perspectives on identifying technological disruption, however, and the path is a little easier. You don’t need to be 51% right in this game. You can be 1% right by finding that one big idea and that’s enough to take care of the other 99% where you got things wrong. Just ask anyone who has been overweight a FANG stock or two for the last few years.

So with a lighter step than the one we typically use to trudge through this topic, let’s review some typical behavioral biases and how they may derail the early discovery of disruptive technology.

#1. Anchoring: the habit of using one piece of information (usually the first thing you see or hear) when making decisions.

Example: Myspace was first to the party in social media, and became the anchor for many people’s perception of what was possible in the space. Then came Facebook…

#2. Availability Heuristic: we look for data in our everyday lives and assess new ideas based on that limited sample.

Example: When the first iPhone was released, it was a cool but expensive phone with some rudimentary web access, limited processing power and a basic camera. Now, the average American spends 4 hours a day on their smartphone, something unimaginable a decade ago. And people all over the world use it to connect to social media, transfer money, and generally manage their lives.

#3. Survivorship bias: focusing on the winners (they get all the press, after all) but not seeking out information about the losers.

Example: I am always surprised when people talk about Tesla but don’t know about DeLorean or Tucker. The latter had persistent problems with access to capital, meeting production targets, and vehicle quality. Does any of that sound familiar? Now, Tesla may end up just fine… But anyone who knew about the losers would have been prepared for the current controversies.

#4. Confirmation bias: only taking in information that supports your existing ideas.

Example: oh, so many here… New consumer technology sparks intellectual tribalism more than almost any topic including American politics. Tesla (again) is a great case study.

#5: Smart money bias: OK, we sort of made this one up but it is so prevalent I doubt you’ll take issue. It is the belief that anything Softbank, Andreessen Horowitz, or any other big-money VC does is, by definition, going to work big time. Venture capital doesn’t work that way. There are a few 100-baggers, lots of so-so, and some outright losses.

Example: solar power Solyndra lost $1 billion of capital provided by the likes of Virgin, US Ventures (one of the oldest VCs in the Valley) and yes, the US Government. Battery company Better Place lost $850 million, provided by Morgan Stanley, Lazard, and HSBC among others.

Summing up: analyzing disruptive technology carries all the behavioral pitfalls of any other human endeavor. The good news is you only need to be right a few times.

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