Are You Climbing the Wrong Hill?

On my recent Disrupt Yourself podcast with Scott Barry Kaufman, I told the story about how, when my daughter was around 10 or 11 years old, she told us that she wanted to be a fiction writer.

As she went through middle school and high school, her focus shifted to excelling academically, getting fives on her AP tests, and gaining admission into the best college. 

She had her focus on climbing an academic hill. 

Then, when the pandemic hit and all the rules changed, it caused her to pause and consider if she was climbing the right hill. 

She wasn’t. 

She decided creative writing really was what she wanted to do. 

Now, she is studying and climbing a hill that will hopefully lead to her becoming a fiction writer. 

We frequently see people, like my daughter, who has realized the S Curve they are on is no longer leading to where they want to be. It’s a common problem. They are still in consulting when they want to be in finance. They are doing computer analytics when they want to be managing people. 

My daughter decided to jump to a new S Curve. But a lot of people don’t. 

Why do smart, driven people stay on an S Curve that no longer aligns with their long-term ambitions? 

In Chris Dixon’s “Climbing the wrong hill” article, he suggests we approach this question by considering a classic computer science problem—hill climbing. It’s a mathematical optimization technique in the local search family. It is an iterative algorithm that starts with an arbitrary solution to a problem and then attempts to find a better answer by making an incremental change to the original solution. 

What does that mean, and why is it called hill climbing? 

Imagine you are dropped at a random spot on hilly terrain, and it’s so foggy you can only see a few feet in front of you. Your goal is to get to the top of the highest hill. 

The first solution is never to take a step that doesn’t take you higher–always travel up. The risk is that if you start near a lower hill, you’ll never end up at the top of the highest hill. Sometimes, you have to go down to go up.

A slightly better solution is to add randomness at the beginning of your climb and reduce it over time, increasing your chances of climbing the tallest hill rather than a shorter one. 

An even better algorithm has you repeatedly drop yourself in random parts of the terrain. At each drop location, you climb a bit, get a feel for the hill's height, and then drop into a new, random area. After many attempts, you determine which of the hills seems the tallest and climb it. 

The challenge is that we, as humans, like each of our steps to be uphill. We stick with the simplest solution because we get the immediate results of taking another step uphill rather than continuously dropping into new places, hiking a bit, and starting over again. 

Research by behavioral economists has shown that people often overvalue near-term over long-term rewards. 

And this effect tends to be even stronger in more ambitious people. 

It is hard—excruciatingly difficult at times—for ambitious people to forgo the nearby upward step, even when they know it’s not the right hill. 

One of the seven accelerants for growth—the tools that help you increase the speed at which you climb an S Curve—is step back to grow. Sometimes, to disrupt yourself, you may have to take a few steps back or sideways to ultimately move forward. 

Computer science has taught us that you have very low odds of finding the highest hill if you only move in one direction. 

When you find the highest hill—when you realize your dream is to write a fiction novel—stop wasting your time climbing the current hill—take the time to find the hill you want to climb.

And, if you find a higher hill—it’s very possible my daughter will—then stop climbing and move hills once again.

Are you climbing the wrong hill? 

Where do you want to “drop” next to explore new terrain? 

How can you step back to grow?